Relevant data representation by a Kernel-based framework

Nowadays, the analysis of a large amount of data has emerged as an issue of great interest taking increasing place in the scientific community, especially in automation, signal processing, pattern recognition, and machine learning. In this sense, the identification, description, classification, visualization, and clustering of events or patterns are important problems for engineering developments and scientific issues, such as biology, medicine, economy, artificial vision, artificial intelligence, and industrial production. Nonetheless, it is difficult to interpret the available information due to its complexity and a large amount of obtained features. In addition, the analysis of the input data requires the development of methodologies that allow to reveal the relevant behaviors of the studied process, particularly, when such signals contain hidden structures varying over a given domain, e.g., space and/or time. When the analyzed signal contains such kind of properties, directly applying signal processing and machine learning procedures without considering a suitable model that deals with both the statistical distribution and the data structure, can lead in unstable performance results. Regarding this, kernel functions appear as an alternative approach to address the aforementioned issues by providing flexible mathematical tools that allow enhancing data representation for supporting signal processing and machine learning systems. Moreover, kernelbased methods are powerful tools for developing better-performing solutions by adapting the kernel to a given problem, instead of learning data relationships from explicit raw vector representations. However, building suitable kernels requires some user prior knowledge about input data, which is not available in most of the practical cases. Furthermore, using the definitions of traditional kernel methods directly, possess a challenging estimation problem that often leads to strong simplifications that restrict the kind of representation that we can use on the data. In this study, we propose a data representation framework based on kernel methods to learn automatically relevant sample relationships in learning systems. Namely, the proposed framework is divided into five kernel-based approaches, which aim to compute relevant data representations by adapting them according to both the imposed sample relationships constraints and the learning scenario (unsupervised or supervised task). First, we develop a kernel-based representation approach that allows revealing the main input sample relations by including relevant data structures using graph-based sparse constraints. Thus, salient data structures are highlighted aiming to favor further unsupervised clustering stages. This approach can be viewed as a graph pruning strategy within a spectral clustering framework which allows enhancing both the local and global data consistencies for a given input similarity matrix. Second, we introduce a kernel-based representation methodology that captures meaningful data relations in terms of their statistical distribution. Thus, an information theoretic learning (ITL) based penalty function is introduced to estimate a kernel-based similarity that maximizes the whole information potential variability. So, we seek for a reproducing kernel Hilbert space (RKHS) that spans the widest information force magnitudes among data points to support further clustering stages. Third, an entropy-like functional on positive definite matrices based on Renyi’s definition is adapted to develop a kernel-based representation approach which considers the statistical distribution and the salient data structures. Thereby, relevant input patterns are highlighted in unsupervised learning tasks. Particularly, the introduced approach is tested as a tool to encode relevant local and global input data relationships in dimensional reduction applications. Fourth, a supervised kernel-based representation is introduced via a metric learning procedure in RKHS that takes advantage of the user-prior knowledge, when available, regarding the studied learning task. Such an approach incorporates the proposed ITL-based kernel functional estimation strategy to adapt automatically the relevant representation using both the supervised information and the input data statistical distribution. As a result, relevant sample dependencies are highlighted by weighting the input features that mostly encode the supervised learning task. Finally, a new generalized kernel-based measure is proposed by taking advantage of different RKHSs. In this way, relevant dependencies are highlighted automatically by considering the input data domain-varying behavior and the user-prior knowledge (supervised information) when available. The proposed measure is an extension of the well-known crosscorrentropy function based on Hilbert space embeddings. Throughout the study, the proposed kernel-based framework is applied to biosignal and image data as an alternative to support aided diagnosis systems and image-based object analysis. Indeed, the introduced kernel-based framework improve, in most of the cases, unsupervised and supervised learning performances, aiding researchers in their quest to process and to favor the understanding of complex dataResumen: Hoy en día, el análisis de datos se ha convertido en un tema de gran interés para la comunidad científica, especialmente en campos como la automatización, el procesamiento de señales, el reconocimiento de patrones y el aprendizaje de máquina. En este sentido, la identificación, descripción, clasificación, visualización, y la agrupación de eventos o patrones son problemas importantes para desarrollos de ingeniería y cuestiones científicas, tales como: la biología, la medicina, la economía, la visión artificial, la inteligencia artificial y la producción industrial. No obstante, es difícil interpretar la información disponible debido a su complejidad y la gran cantidad de características obtenidas. Además, el análisis de los datos de entrada requiere del desarrollo de metodologías que permitan revelar los comportamientos relevantes del proceso estudiado, en particular, cuando tales señales contienen estructuras ocultas que varían sobre un dominio dado, por ejemplo, el espacio y/o el tiempo. Cuando la señal analizada contiene este tipo de propiedades, los rendimientos pueden ser inestables si se aplican directamente técnicas de procesamiento de señales y aprendizaje automático sin tener en cuenta la distribución estadística y la estructura de datos. Al respecto, las funciones núcleo (kernel) aparecen como un enfoque alternativo para abordar las limitantes antes mencionadas, proporcionando herramientas matemáticas flexibles que mejoran la representación de los datos de entrada. Por otra parte, los métodos basados en funciones núcleo son herramientas poderosas para el desarrollo de soluciones de mejor rendimiento mediante la adaptación del núcleo de acuerdo al problema en estudio. Sin embargo, la construcción de funciones núcleo apropiadas requieren del conocimiento previo por parte del usuario sobre los datos de entrada, el cual no está disponible en la mayoría de los casos prácticos. Por otra parte, a menudo la estimación de las funciones núcleo conllevan sesgos el modelo, siendo necesario apelar a simplificaciones matemáticas que no siempre son acordes con la realidad. En este estudio, se propone un marco de representación basado en métodos núcleo para resaltar relaciones relevantes entre los datos de forma automática en sistema de aprendizaje de máquina. A saber, el marco propuesto consta de cinco enfoques núcleo, en aras de adaptar la representación de acuerdo a las relaciones impuestas sobre las muestras y sobre el escenario de aprendizaje (sin/con supervisión). En primer lugar, se desarrolla un enfoque de representación núcleo que permite revelar las principales relaciones entre muestras de entrada mediante la inclusión de estructuras relevantes utilizando restricciones basadas en modelado por grafos. Por lo tanto, las estructuras de datos más sobresalientes se destacan con el objetivo de favorecer etapas posteriores de agrupamiento no supervisado. Este enfoque puede ser visto como una estrategia de depuración de grafos dentro de un marco de agrupamiento espectral que permite mejorar las consistencias locales y globales de los datos En segundo lugar, presentamos una metodología de representación núcleo que captura relaciones significativas entre muestras en términos de su distribución estadística. De este modo, se introduce una función de costo basada en aprendizaje por teoría de la información para estimar una similitud que maximice la variabilidad del potencial de información de los datos de entrada. Así, se busca un espacio de Hilbert generado por el núcleo que contenga altas fuerzas de información entre los puntos para favorecer el agrupamiento entre los mismos. En tercer lugar, se propone un esquema de representación que incluye un funcional de entropía para matrices definidas positivas a partir de la definición de Renyi. En este sentido, se pretenden incluir la distribución estadística de las muestras y sus estructuras relevantes. Por consiguiente, los patrones de entrada pertinentes se destacan en tareas de aprendizaje sin supervisión. En particular, el enfoque introducido se prueba como una herramienta para codificar las relaciones locales y globales de los datos en tareas de reducción de dimensión. En cuarto lugar, se introduce una metodología de representación núcleo supervisada a través de un aprendizaje de métrica en el espacio de Hilbert generado por una función núcleo en aras de aprovechar el conocimiento previo del usuario con respecto a la tarea de aprendizaje. Este enfoque incorpora un funcional por teoría de información que permite adaptar automáticamente la representación utilizando tanto información supervisada y la distribución estadística de los datos de entrada. Como resultado, las dependencias entre las muestras se resaltan mediante la ponderación de las características de entrada que codifican la tarea de aprendizaje supervisado. Por último, se propone una nueva medida núcleo mediante el aprovechamiento de diferentes espacios de representación. De este modo, las dependencias más relevantes entre las muestras se resaltan automáticamente considerando el dominio de interés de los datos de entrada y el conocimiento previo del usuario (información supervisada). La medida propuesta es una extensión de la función de cross-correntropia a partir de inmersiones en espacios de Hilbert. A lo largo del estudio, el esquema propuesto se valida sobre datos relacionados con bioseñales e imágenes como una alternativa para apoyar sistemas de apoyo diagnóstico y análisis objetivo basado en imágenes. De hecho, el marco introducido permite mejorar, en la mayoría de los casos, el rendimiento de sistemas de aprendizaje supervisado y no supervisado, favoreciendo la precisión de la tarea y la interpretabilidad de los datosDoctorad

Video Classification System Based on Representation Techniques Using Kernel Methods and Bayesian Inference

En este trabajo propusimos diferentes estrategias de representación de características para el procesamiento de video. Nuestro principal objetivo es revelar patrones discriminantes de datos de video para mejorar la tarea de visión por computadora, reconocimiento de acción humana. Con este fin, propusimos utilizar un análisis de relevancia basado en un núcleo para reconocer los descriptores mas relevantes relacionados con el conocimiento de acciones. Además, la propuesta permite calcular una matriz de proyección lineal para mapear muestras de video en un nuevo espacio, donde se preserva la separabilidad de clases y se reduce la dimensionalidad de la representación

Minimising Human Annotation for Scalable Person Re-Identification

PhDAmong the diverse tasks performed by an intelligent distributed multi-camera surveillance system, person re-identification (re-id) is one of the most essential. Re-id refers to associating an individual or a group of people across non-overlapping cameras at different times and locations, and forms the foundation of a variety of applications ranging from security and forensic search to quotidian retail and health care. Though attracted rapidly increasing academic interests over the past decade, it still remains a non-trivial and unsolved problem for launching a practical reid system in real-world environments, due to the ambiguous and noisy feature of surveillance data and the potentially dramatic visual appearance changes caused by uncontrolled variations in human poses and divergent viewing conditions across distributed camera views. To mitigate such visual ambiguity and appearance variations, most existing re-id approaches rely on constructing fully supervised machine learning models with extensively labelled training datasets which is unscalable for practical applications in the real-world. Particularly, human annotators must exhaustively search over a vast quantity of offline collected data, manually label cross-view matched images of a large population between every possible camera pair. Nonetheless, having the prohibitively expensive human efforts dissipated, a trained re-id model is often not easily generalisable and transferable, due to the elastic and dynamic operating conditions of a surveillance system. With such motivations, this thesis proposes several scalable re-id approaches with significantly reduced human supervision, readily applied to practical applications. More specifically, this thesis has developed and investigated four new approaches for reducing human labelling effort in real-world re-id as follows: Chapter 3 The first approach is affinity mining from unlabelled data. Different from most existing supervised approaches, this work aims to model the discriminative information for reid without exploiting human annotations, but from the vast amount of unlabelled person image data, thus applicable to both semi-supervised and unsupervised re-id. It is non-trivial since the human annotated identity matching correspondence is often the key to discriminative re-id modelling. In this chapter, an alternative strategy is explored by specifically mining two types of affinity relationships among unlabelled data: (1) inter-view data affinity and (2) intra-view data affinity. In particular, with such affinity information encoded as constraints, a Regularised Kernel Subspace Learning model is developed to explicitly reduce inter-view appearance variations and meanwhile enhance intra-view appearance disparity for more discriminative re-id matching. Consequently, annotation costs can be immensely alleviated and a scalable re-id model is readily to be leveraged to plenty of unlabelled data which is inexpensive to collect. Chapter 4 The second approach is saliency discovery from unlabelled data. This chapter continues to investigate the problem of what can be learned in unlabelled images without identity labels annotated by human. Other than affinity mining as proposed by Chapter 3, a different solution is proposed. That is, to discover localised visual appearance saliency of person appearances. Intuitively, salient and atypical appearances of human are able to uniquely and representatively describe and identify an individual, whilst also often robust to view changes and detection variances. Motivated by this, an unsupervised Generative Topic Saliency model is proposed to jointly perform foreground extraction, saliency detection, as well as discriminative re-id matching. This approach completely avoids the exhaustive annotation effort for model training, and thus better scales to real-world applications. Moreover, its automatically discovered re-id saliency representations are shown to be semantically interpretable, suitable for generating useful visual analysis for deployable user-oriented software tools. Chapter 5 The third approach is incremental learning from actively labelled data. Since learning from unlabelled data alone yields less discriminative matching results, and in some cases there will be limited human labelling resources available for re-id modelling, this chapter thus investigate the problem of how to maximise a model’s discriminative capability with minimised labelling efforts. The challenges are to (1) automatically select the most representative data from a vast number of noisy/ambiguous unlabelled data in order to maximise model discrimination capacity; and (2) incrementally update the model parameters to accelerate machine responses and reduce human waiting time. To that end, this thesis proposes a regression based re-id model, characterised by its very fast and efficient incremental model updates. Furthermore, an effective active data sampling algorithm with three novel joint exploration-exploitation criteria is designed, to make automatic data selection feasible with notably reduced human labelling costs. Such an approach ensures annotations to be spent only on very few data samples which are most critical to model’s generalisation capability, instead of being exhausted by blindly labelling many noisy and redundant training samples. Chapter 6 The last technical area of this thesis is human-in-the-loop learning from relevance feedback. Whilst former chapters mainly investigate techniques to reduce human supervision for model training, this chapter motivates a novel research area to further minimise human efforts spent in the re-id deployment stage. In real-world applications where camera network and potential gallery size increases dramatically, even the state-of-the-art re-id models generate much inferior re-id performances and human involvements at deployment stage is inevitable. To minimise such human efforts and maximise re-id performance, this thesis explores an alternative approach to re-id by formulating a hybrid human-computer learning paradigm with humans in the model matching loop. Specifically, a Human Verification Incremental Learning model is formulated which does not require any pre-labelled training data, therefore scalable to new camera pairs; Moreover, the proposed model learns cumulatively from human feedback to provide an instant improvement to re-id ranking of each probe on-the-fly, thus scalable to large gallery sizes. It has been demonstrated that the proposed re-id model achieves significantly superior re-id results whilst only consumes much less human supervision effort. For facilitating a holistic understanding about this thesis, the main studies are summarised and framed into a graphical abstract as shown in Figur

Robust online subspace learning

In this thesis, I aim to advance the theories of online non-linear subspace learning through the development of strategies which are both efficient and robust. The use of subspace learning methods is very popular in computer vision and they have been employed to numerous tasks. With the increasing need for real-time applications, the formulation of online (i.e. incremental and real-time) learning methods is a vibrant research field and has received much attention from the research community. A major advantage of incremental systems is that they update the hypothesis during execution, thus allowing for the incorporation of the real data seen in the testing phase. Tracking acts as an attractive and popular evaluation tool for incremental systems, and thus, the connection between online learning and adaptive tracking is seen commonly in the literature. The proposed system in this thesis facilitates learning from noisy input data, e.g. caused by occlusions, casted shadows and pose variations, that are challenging problems in general tracking frameworks. First, a fast and robust alternative to standard L2-norm principal component analysis (PCA) is introduced, which I coin Euler PCA (e-PCA). The formulation of e-PCA is based on robust, non-linear kernel PCA (KPCA) with a cosine-based kernel function that is expressed via an explicit feature space. When applied to tracking, face reconstruction and background modeling, promising results are achieved. In the second part, the problem of matching vectors of 3D rotations is explicitly targeted. A novel distance which is robust for 3D rotations is introduced, and formulated as a kernel function. The kernel leads to a new representation of 3D rotations, the full-angle quaternion (FAQ) representation. Finally, I propose 3D object recognition from point clouds, and object tracking with color values using FAQs. A domain-specific kernel function designed for visual data is then presented. KPCA with Krein space kernels is introduced, as this kernel is indefinite, and an exact incremental learning framework for the new kernel is developed. In a tracker framework, the presented online learning outperforms the competitors in nine popular and challenging video sequences. In the final part, the generalized eigenvalue problem is studied. Specifically, incremental slow feature analysis (SFA) with indefinite kernels is proposed, and applied to temporal video segmentation and tracking with change detection. As online SFA allows for drift detection, further improvements are achieved in the evaluation of the tracking task.Open Acces

Neuroengineering of Clustering Algorithms

Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv

DIAGNOSTICS OF DEMENTIA FROM STRUCTURAL AND FUNCTIONAL MARKERS OF BRAIN ATROPHY WITH MACHINE LEARNING

Dementia is a condition in which higher mental functions are disrupted. It currently affects an estimated 57 million people throughout the world. A dementia diagnosis is difficult since neither anatomical indicators nor functional testing is currently sufficiently sensitive or specific. There remains a long list of outstanding issues that must be addressed. First, multimodal diagnosis has yet to be introduced into the early stages of dementia screening. Second, there is no accurate instrument for predicting the progression of pre-dementia. Third, non-invasive testing cannot be used to provide differential diagnoses. By creating ML models of normal and accelerated brain aging, we intend to better understand brain development. The combined analysis of distinct imaging and functional modalities will improve diagnostics of accelerated decline with advanced data science techniques, which is the main objective of our study. Hypothetically, an association between brain structural changes and cognitive performance differs between normal and accelerated aging. We propose using brain MRI scans to estimate the cognitive status of the cognitively preserved examinee and develop a structure-function model with machine learning (ML). Accelerated ageing is suspected when a scanned individual’s findings do not align with the usual paradigm. We calculate the deviation from the model of normal ageing (DMNA) as the error of cognitive score prediction. Then the obtained data may be compared with the results of conducted cognitive tests. The greater the difference between the expected and observed values, the greater the risk of dementia. DMNA can discern between cognitively normal and mild cognitive impairment (MCI) patients. The model was proven to perform well in the MCI-versus-Alzheimer’s disease (AD) categorization. DMNA is a potential diagnostic marker of dementia and its types

ACTIVATED CARBON NANOFIBERS FROM RENEWABLE (LIGNIN) AND WASTE RESOURCES (RECYCLED PET) AND THEIR ADSORPTION CAPACITY OF REFRACTORY SULFUR COMPOUNDS FROM FOSSIL FUELS

Dementia is a condition in which higher mental functions are disrupted. It currently affects an estimated 57 million people throughout the world. Dementia diagnosis is difficult since neither anatomical indicator nor functional testing are currently sufficiently sensitive or specific. There remains a long list of outstanding issues that must be addressed. First, multimodal diagnosis has yet to be introduced into the early stages of dementia screening. Second, there is no accurate instrument for predicting the progression of pre-dementia. Third, non-invasive testing cannot be used to provide differential diagnoses. By creating ML models of normal and accelerated brain aging, we intend to better understand brain development. The combined analysis of distinct imaging and functional modalities will improve diagnostics of accelerated decline with advanced data science techniques, which is the main objective of our study. Hypothetically, an association between brain structural changes and cognitive performance differs between normal and accelerated aging. We propose using brain MRI scans to estimate the cognitive status of the cognitively preserved examinee and develop a structure-function model with machine learning (ML). Accelerated aging is suspected when a scanned individual’s findings do not align with the usual paradigm. We calculate the deviation from the model of normal aging (DMNA) as the error of cognitive score prediction. Then the obtained data may be compared with the results of conducted cognitive tests. The greater the difference between the expected and observed values, the greater the risk of dementia. DMNA can discern between cognitively normal and mild cognitive impairment (MCI) patients. The model was proven to perform well in the MCI-versus-Alzheimer’s disease (AD) categorization. DMNA is a potential diagnostic marker of dementia and its types