Data Fusion for MaaS: Opportunities and Challenges

© 2018 IEEE. Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling

Doctor of Philosophy

dissertationLatent structures play a vital role in many data analysis tasks. By providing compact yet expressive representations, such structures can offer useful insights into the complex and high-dimensional datasets encountered in domains such as computational biology, computer vision, natural language processing, etc. Specifying the right complexity of these latent structures for a given problem is an important modeling decision. Instead of using models with an a priori fixed complexity, it is desirable to have models that can adapt their complexity as the data warrant. Nonparametric Bayesian models are motivated precisely based on this desideratum by offering a flexible modeling paradigm for data without limiting the model-complexity a priori. The flexibility comes from the model's ability to adjust its complexity adaptively with data. This dissertation is about nonparametric Bayesian learning of two specific types of latent structures: (1) low-dimensional latent features underlying high-dimensional observed data where the latent features could exhibit interdependencies, and (2) latent task structures that capture how a set of learning tasks relate with each other, a notion critical in the paradigm of Multitask Learning where the goal is to solve multiple learning tasks jointly in order to borrow information across similar tasks. Another focus of this dissertation is on designing efficient approximate inference algorithms for nonparametric Bayesian models. Specifically, for the nonparametric Bayesian latent feature model where the goal is to infer the binary-valued latent feature assignment matrix for a given set of observations, the dissertation proposes two approximate inference methods. The first one is a search-based algorithm to find the maximum-a-posteriori (MAP) solution for the latent feature assignment matrix. The second one is a sequential Monte-Carlo-based approximate inference algorithm that allows processing the data oneexample- at-a-time while being space-efficient in terms of the storage required to represent the posterior distribution of the latent feature assignment matrix

Action recognition in visual sensor networks: a data fusion perspective

Visual Sensor Networks have emerged as a new technology to bring computer vision algorithms to the real world. However, they impose restrictions in the computational resources and bandwidth available to solve target problems. This thesis is concerned with the definition of new efficient algorithms to perform Human Action Recognition with Visual Sensor Networks. Human Action Recognition systems apply sequence modelling methods to integrate the temporal sensor measurements available. Among sequence modelling methods, the Hidden Conditional Random Field has shown a great performance in sequence classification tasks, outperforming many other methods. However, a parameter estimation procedure has not been proposed with feature and model selection properties. This thesis fills this lack proposing a new objective function to optimize during training. The L2 regularizer employed in the standard objective function is replaced by an overlapping group-L1 regularizer that produces feature and model selection effects in the optima. A gradient-based search strategy is proposed to find the optimal parameters of the objective function. Experimental evidence shows that Hidden Conditional Random Fields with their parameters estimated employing the proposed method have a higher predictive accuracy than those estimated with the standard method, with an smaller inference cost. This thesis also deals with the problem of human action recognition from multiple cameras, with the focus on reducing the amount of network bandwidth required. A multiple view dimensionality reduction framework is developed to obtain similar low dimensional representation for the motion descriptors extracted from multiple cameras. An alternative is proposed predicting the action class locally at each camera with the motion descriptors extracted from each view and integrating the different action decisions to make a global decision on the action performed. The reported experiments show that the proposed framework has a predictive performance similar to 3D state of the art methods, but with a lower computational complexity and lower bandwidth requirements. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Las Redes de Sensores Visuales son una nueva tecnología que permite el despliegue de algoritmos de visión por computador en el mundo real. Sin embargo, estas imponen restricciones en los recursos de computo y de ancho de banda disponibles para la resolución del problema en cuestión. Esta tesis tiene por objeto la definición de nuevos algoritmos con los que realizar reconocimiento de actividades humanas en redes de sensores visuales, teniendo en cuenta las restricciones planteadas. Los sistemas de reconocimiento de acciones aplican métodos de modelado de secuencias para la integración de las medidas temporales proporcionadas por los sensores. Entre los modelos para el modelado de secuencias, el Hidden Conditional Random Field a mostrado un gran rendimiento en la clasificación de secuencias, superando a otros métodos existentes. Sin embargo, no se ha definido un procedimiento para la integración de sus parámetros que incluya selección de atributos y selección de modelo. Esta tesis tiene por objeto cubrir esta carencia proponiendo una nueva función objetivo para optimizar durante la estimación de los parámetros obtimos. El regularizador L2 empleado en la función objetivo estandar se va a remplazar for un regularizador grupo-L1 solapado que va a producir los efectos de selección de modelo y atributos deseados en el óptimo. Se va a proponer una estrategia de búsqueda con la que obtener el valor óptimo de estos parámetros. Los experimentos realizados muestran que los modelos estimados utilizando la función objetivo prouesta tienen un mayor poder de predicción, reduciendo al mismo tiempo el coste computacional de la inferencia. Esta tesis también trata el problema del reconocimiento de acciones humanas emepleando multiples cámaras, centrándonos en reducir la cantidad de ancho de banda requerido par el proceso. Para ello se propone un nueva estructura en la que definir algoritmos de reducción de dimensionalidad para datos definidos en multiples vistas. Mediante su aplicación se obtienen representaciones de baja dimensionalidad similares para los descriptores de movimiento calculados en cada una de las cámaras.También se propone un método alternativo basado en la predicción de la acción realizada con los descriptores obtenidos en cada una de las cámaras, para luego combinar las diferentes predicciones en una global. La experimentación realizada muestra que estos métodos tienen una eficacia similar a la alcanzada por los métodos existentes basados en reconstrucción 3D, pero con una menor complejidad computacional y un menor uso de la red

Tensor-based regression models and applications

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2017-2018Avec l’avancement des technologies modernes, les tenseurs d’ordre élevé sont assez répandus et abondent dans un large éventail d’applications telles que la neuroscience informatique, la vision par ordinateur, le traitement du signal et ainsi de suite. La principale raison pour laquelle les méthodes de régression classiques ne parviennent pas à traiter de façon appropriée des tenseurs d’ordre élevé est due au fait que ces données contiennent des informations structurelles multi-voies qui ne peuvent pas être capturées directement par les modèles conventionnels de régression vectorielle ou matricielle. En outre, la très grande dimensionnalité de l’entrée tensorielle produit une énorme quantité de paramètres, ce qui rompt les garanties théoriques des approches de régression classique. De plus, les modèles classiques de régression se sont avérés limités en termes de difficulté d’interprétation, de sensibilité au bruit et d’absence d’unicité. Pour faire face à ces défis, nous étudions une nouvelle classe de modèles de régression, appelés modèles de régression tensor-variable, où les prédicteurs indépendants et (ou) les réponses dépendantes prennent la forme de représentations tensorielles d’ordre élevé. Nous les appliquons également dans de nombreuses applications du monde réel pour vérifier leur efficacité et leur efficacité.With the advancement of modern technologies, high-order tensors are quite widespread and abound in a broad range of applications such as computational neuroscience, computer vision, signal processing and so on. The primary reason that classical regression methods fail to appropriately handle high-order tensors is due to the fact that those data contain multiway structural information which cannot be directly captured by the conventional vector-based or matrix-based regression models, causing substantial information loss during the regression. Furthermore, the ultrahigh dimensionality of tensorial input produces huge amount of parameters, which breaks the theoretical guarantees of classical regression approaches. Additionally, the classical regression models have also been shown to be limited in terms of difficulty of interpretation, sensitivity to noise and absence of uniqueness. To deal with these challenges, we investigate a novel class of regression models, called tensorvariate regression models, where the independent predictors and (or) dependent responses take the form of high-order tensorial representations. We also apply them in numerous real-world applications to verify their efficiency and effectiveness. Concretely, we first introduce hierarchical Tucker tensor regression, a generalized linear tensor regression model that is able to handle potentially much higher order tensor input. Then, we work on online local Gaussian process for tensor-variate regression, an efficient nonlinear GPbased approach that can process large data sets at constant time in a sequential way. Next, we present a computationally efficient online tensor regression algorithm with general tensorial input and output, called incremental higher-order partial least squares, for the setting of infinite time-dependent tensor streams. Thereafter, we propose a super-fast sequential tensor regression framework for general tensor sequences, namely recursive higher-order partial least squares, which addresses issues of limited storage space and fast processing time allowed by dynamic environments. Finally, we introduce kernel-based multiblock tensor partial least squares, a new generalized nonlinear framework that is capable of predicting a set of tensor blocks by merging a set of tensor blocks from different sources with a boosted predictive power

Representation Learning for Words and Entities

This thesis presents new methods for unsupervised learning of distributed representations of words and entities from text and knowledge bases. The first algorithm presented in the thesis is a multi-view algorithm for learning representations of words called Multiview Latent Semantic Analysis (MVLSA). By incorporating up to 46 different types of co-occurrence statistics for the same vocabulary of english words, I show that MVLSA outperforms other state-of-the-art word embedding models. Next, I focus on learning entity representations for search and recommendation and present the second method of this thesis, Neural Variational Set Expansion (NVSE). NVSE is also an unsupervised learning method, but it is based on the Variational Autoencoder framework. Evaluations with human annotators show that NVSE can facilitate better search and recommendation of information gathered from noisy, automatic annotation of unstructured natural language corpora. Finally, I move from unstructured data and focus on structured knowledge graphs. I present novel approaches for learning embeddings of vertices and edges in a knowledge graph that obey logical constraints.Comment: phd thesis, Machine Learning, Natural Language Processing, Representation Learning, Knowledge Graphs, Entities, Word Embeddings, Entity Embedding

Multiview pattern recognition methods for data visualization, embedding and clustering

Multiview data is defined as data for whose samples there exist several different data views, i.e. different data matrices obtained through different experiments, methods or situations. Multiview dimensionality reduction methods transform a high­dimensional, multiview dataset into a single, low-dimensional space or projection. Their goal is to provide a more manageable representation of the original data, either for data visualization or to simplify the following analysis stages. Multiview clustering methods receive a multiview dataset and propose a single clustering assignment of the data samples in the dataset, considering the information from all the input data views. The main hypothesis defended in this work is that using multiview data along with methods able to exploit their information richness produces better dimensionality reduction and clustering results than simply using single views or concatenating all views into a single matrix. Consequently, the objectives of this thesis are to develop and test multiview pattern recognition methods based on well known single-view dimensionality reduction and clustering methods. Three multiview pattern recognition methods are presented: multiview t-distributed stochastic neighbourhood embedding (MV-tSNE), multiview multimodal scaling (MV-MDS) and a novel formulation of multiview spectral clustering (MVSC-CEV). These methods can be applied both to dimensionality reduction tasks and to clustering tasks. The MV-tSNE method computes a matrix of probabilities based on distances between sam ples for each input view. Then it merges the different probability matrices using results from expert opinion pooling theory to get a common matrix of probabilities, which is then used as reference to build a low-dimensional projection of the data whose probabilities are similar. The MV-MDS method computes the common eigenvectors of all the normalized distance matrices in order to obtain a single low-dimensional space that embeds the essential information from all the input spaces, avoiding redundant information to be included. The MVSC-CEV method computes the symmetric Laplacian matrices of the similaritymatrices of all data views. Then it generates a single, low-dimensional representation of the input data by computing the common eigenvectors of the Laplacian matrices, obtaining a projection of the data that embeds the most relevan! information of the input data views, also avoiding the addition of redundant information. A thorough set of experiments has been designed and run in order to compare the proposed methods with their single view counterpart. Also, the proposed methods have been compared with all the available results of equivalent methods in the state of the art. Finally, a comparison between the three proposed methods is presented in order to provide guidelines on which method to use for a given task. MVSC-CEV consistently produces better clustering results than other multiview methods in the state of the art. MV-MDS produces overall better results than the reference methods in dimensionality reduction experiments. MV-tSNE does not excel on any of these tasks. As a consequence, for multiview clustering tasks it is recommended to use MVSC-CEV, and MV-MDS for multiview dimensionality reduction tasks. Although several multiview dimensionality reduction or clustering methods have been proposed in the state of the art, there is no software implementation available. In order to compensate for this fact and to provide the communitywith a potentially useful set of multiview pattern recognition methods, an R software package containg the proposed methods has been developed and released to the public.Los datos multivista se definen como aquellos datos para cuyas muestras existen varias vistas de datos distintas , es decir diferentes matrices de datos obtenidas mediante diferentes experimentos , métodos o situaciones. Los métodos multivista de reducción de la dimensionalidad transforman un conjunto de datos multivista y de alta dimensionalidad en un único espacio o proyección de baja dimensionalidad. Su objetivo es producir una representación más manejable de los datos originales, bien para su visualización o para simplificar las etapas de análisis subsiguientes. Los métodos de agrupamiento multivista reciben un conjunto de datos multivista y proponen una única asignación de grupos para sus muestras, considerando la información de todas las vistas de datos de entrada. La principal hipótesis defendida en este trabajo es que el uso de datos multivista junto con métodos capaces de aprovechar su riqueza informativa producen mejores resultados en reducción de la dimensionalidad y agrupamiento frente al uso de vistas únicas o la concatenación de varias vistas en una única matriz. Por lo tanto, los objetivos de esta tesis son desarrollar y probar métodos multivista de reconocimiento de patrones basados en métodos univista reconocidos. Se presentan tres métodos multivista de reconocimiento de patrones: proyección estocástica de vecinos multivista (MV-tSNE), escalado multidimensional multivista (MV-MDS) y una nueva formulación de agrupamiento espectral multivista (MVSC-CEV). Estos métodos pueden aplicarse tanto a tareas de reducción de la dimensionalidad como a de agrupamiento. MV-tSNE calcula una matriz de probabilidades basada en distancias entre muestras para cada vista de datos. A continuación combina las matrices de probabilidad usando resultados de la teoría de combinación de expertos para obtener una matriz común de probabilidades, que se usa como referencia para construir una proyección de baja dimensionalidad de los datos. MV-MDS calcula los vectores propios comunes de todas las matrices normalizadas de distancia para obtener un único espacio de baja dimensionalidad que integre la información esencial de todos los espacios de entrada, evitando información redundante. MVSC-CEVcalcula las matrices Laplacianas de las matrices de similitud de los datos. A continuación genera una única representación de baja dimensionalidad calculando los vectores propios comunes de las Laplacianas. Así obtiene una proyección de los datos que integra la información más relevante y evita añadir información redundante. Se ha diseñado y ejecutado una batería de experimentos completa para comparar los métodos propuestos con sus equivalentes univista. Además los métodos propuestos se han comparado con los resultados disponibles en la literatura. Finalmente, se presenta una comparación entre los tres métodos para proporcionar orientaciones sobre el método más adecuado para cada tarea. MVSC-CEV produce mejores agrupamientos que los métodos equivalentes en la literatura. MV-MDS produce en general mejores resultados que los métodos de referencia en experimentos de reducción de la dimensionalidad. MV-tSNE no destaca en ninguna de esas tareas . Consecuentemente , para agrupamiento multivista se recomienda usar MVSC-CEV, y para reducción de la dimensionalidad multivista MV-MDS. Aunque se han propuesto varios métodos multivista en la literatura, no existen programas disponibles públicamente. Para remediar este hecho y para dotar a la comunidad de un conjunto de métodos potencialmente útil, se ha desarrollado un paquete de programas en R y se ha puesto a disposición del público