One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

Metabolic profiling on 2D NMR TOCSY spectra using machine learning

Due to the dynamicity of biological cells, the role of metabolic profiling in discovering biological fingerprints of diseases, and their evolution, as well as the cellular pathway of different biological or chemical stimuli is most significant. Two-dimensional nuclear magnetic resonance (2D NMR) is one of the fundamental and strong analytical instruments for metabolic profiling. Though, total correlation spectroscopy (2D NMR 1H -1H TOCSY) can be used to improve spectral overlap of 1D NMR, strong peak shift, signal overlap, spectral crowding and matrix effects in complex biological mixtures are extremely challenging in 2D NMR analysis. In this work, we introduce an automated metabolic deconvolution and assignment based on the deconvolution of 2D TOCSY of real breast cancer tissue, in addition to different differentiation pathways of adipose tissue-derived human Mesenchymal Stem cells. A major alternative to the common approaches in NMR based machine learning where images of the spectra are used as an input, our metabolic assignment is based only on the vertical and horizontal frequencies of metabolites in the 1H-1H TOCSY. One- and multi-class Kernel null foley–Sammon transform, support vector machines, polynomial classifier kernel density estimation, and support vector data description classifiers were tested in semi-supervised learning and novelty detection settings. The classifiers’ performance was evaluated by comparing the conventional human-based methodology and automatic assignments under different initial training sizes settings. The results of our novel metabolic profiling methods demonstrate its suitability, robustness, and speed in automated nontargeted NMR metabolic analysis

On the Recognition of Emotion from Physiological Data

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

Novel Pattern Recognition Techniques for Improved Target Detection in Hyperspectral Imagery

A fundamental challenge in target detection in hyperspectral imagery is spectral variability. In target detection applications, we are provided with a pure target signature; we do not have a collection of samples that characterize the spectral variability of the target. Another problem is that the performance of stochastic detection algorithms such as the spectral matched filter can be detrimentally affected by the assumptions of multivariate normality of the data, which are often violated in practical situations. We address the challenge of lack of training samples by creating two models to characterize the target class spectral variability --the first model makes no assumptions regarding inter-band correlation, while the second model uses a first-order Markovbased scheme to exploit correlation between bands. Using these models, we present two techniques for meeting these challenges-the kernel-based support vector data description (SVDD) and spectral fringe-adjusted joint transform correlation (SFJTC). We have developed an algorithm that uses the kernel-based SVDD for use in full-pixel target detection scenarios. We have addressed optimization of the SVDD kernel-width parameter using the golden-section search algorithm for unconstrained optimization. We investigated a proper number of signatures N to generate for the SVDD target class and found that only a small number of training samples is required relative to the dimensionality (number of bands). We have extended decision-level fusion techniques using the majority vote rule for the purpose of alleviating the problem of selecting a proper value of s 2 for either of our target variability models. We have shown that heavy spectral variability may cause SFJTC-based detection to suffer and have addressed this by developing an algorithm that selects an optimal combination of the discrete wavelet transform (DWT) coefficients of the signatures for use as features for detection. For most scenarios, our results show that our SVDD-based detection scheme provides low false positive rates while maintaining higher true positive rates than popular stochastic detection algorithms. Our results also show that our SFJTC-based detection scheme using the DWT coefficients can yield significant detection improvement compared to use of SFJTC using the original signatures and traditional stochastic and deterministic algorithms

The spatial ecology of an endemic desert shrub

Using spatial patterns to infer biotic and abiotic processes underlying plant population dynamics is an important technique in contemporary ecology, with particular utility when investigating and shrub population dynamics, for which experimental and observational methodologies are rarely feasible. Using a novel one-class classification technique, the locations of over 17,000 Spartocytisus supranubius individuals were mapped from aerial imagery generating a spatially extensive (162 ha), yet accurate, dataset. The recent rapid increase in studies using pattern-process inference has not been accompanied by a rigorous assessment of the behaviour of these techniques, nor an appraisal of their utility in addressing ecological research questions. The first part of the thesis addresses these concerns, investigating whether current methodologies are adequate to test hypotheses concerning spatial interactions. A literature review reveals a preponderance of studies of small, little-replicated plots. The results of the research raise concerns about the utility of spatial point pattern analyses as currently applied in the literature. To avoid inaccurate description of fine-scale spatial structures it is recommended that researchers increase plot replication. Furthermore, studies of spatial structure and population dynamics should account for spatial environmental gradients, whatever plot size is used. The second part of the thesis presents a rigorous investigation, incorporating a priori inference and the application of fine-scale spatial statistical and modelling techniques, of the biotic and abiotic mechanisms underlying the spatial structure and population dynamics of S. supranubius, a leguminous shrub species endemic to the Canary Islands. The spatial structure of S. supranubius populations is consistent with the operation of clonal reproduction and intra-specific competition. However, the results indicate that spatial environmental heterogeneity (from small to broad scales), in particular topography, can interact with biotic processes to generate quantitatively different S. Supranubius patterns in different locations. Future research into the spatial and temporal dynamics of interactions between abiotic and biotic processes is recommended

The spatial ecology of an endemic desert shrub

Using spatial patterns to infer biotic and abiotic processes underlying plant population dynamics is an important technique in contemporary ecology, with particular utility when investigating and shrub population dynamics, for which experimental and observational methodologies are rarely feasible. Using a novel one-class classification technique, the locations of over 17,000 Spartocytisus supranubius individuals were mapped from aerial imagery generating a spatially extensive (162 ha), yet accurate, dataset. The recent rapid increase in studies using pattern-process inference has not been accompanied by a rigorous assessment of the behaviour of these techniques, nor an appraisal of their utility in addressing ecological research questions. The first part of the thesis addresses these concerns, investigating whether current methodologies are adequate to test hypotheses concerning spatial interactions. A literature review reveals a preponderance of studies of small, little-replicated plots. The results of the research raise concerns about the utility of spatial point pattern analyses as currently applied in the literature. To avoid inaccurate description of fine-scale spatial structures it is recommended that researchers increase plot replication. Furthermore, studies of spatial structure and population dynamics should account for spatial environmental gradients, whatever plot size is used. The second part of the thesis presents a rigorous investigation, incorporating a priori inference and the application of fine-scale spatial statistical and modelling techniques, of the biotic and abiotic mechanisms underlying the spatial structure and population dynamics of S. supranubius, a leguminous shrub species endemic to the Canary Islands. The spatial structure of S. supranubius populations is consistent with the operation of clonal reproduction and intra-specific competition. However, the results indicate that spatial environmental heterogeneity (from small to broad scales), in particular topography, can interact with biotic processes to generate quantitatively different S. Supranubius patterns in different locations. Future research into the spatial and temporal dynamics of interactions between abiotic and biotic processes is recommended