Large-scale dimensionality reduction using perturbation theory and singular vectors

Massive volumes of high-dimensional data have become pervasive, with the number of features significantly exceeding the number of samples in many applications. This has resulted in a bottleneck for data mining applications and amplified the computational burden of machine learning algorithms that perform classification or pattern recognition. Dimensionality reduction can handle this problem in two ways, i.e. feature selection (FS) and feature extraction. In this thesis, we focus on FS, because, in many applications like bioinformatics, the domain experts need to validate a set of original features to corroborate the hypothesis of the prediction models. In processing the high-dimensional data, FS mainly involves detecting a limited number of important features among tens/hundreds of thousands of irrelevant and redundant features. We start with filtering the irrelevant features using our proposed Sparse Least Squares (SLS) method, where a score is assigned to each feature, and the low-scoring features are removed using a soft threshold. To demonstrate the effectiveness of SLS, we used it to augment the well-known FS methods, thereby achieving substantially reduced running times while improving or at least maintaining the prediction accuracy of the models. We developed a linear FS method (DRPT) which, upon data reduction by SLS, clusters the reduced data using the perturbation theory to detect correlations between the remaining features. Important features are ultimately selected from each cluster, discarding the redundant features. To extend the clustering applicability in grouping the redundant features, we proposed a new Singular Vectors FS (SVFS) method that is capable of both removing the irrelevant features and effectively clustering the remaining features. As such, the features in each cluster solely exhibit inner correlations with each other. The independently selected important features from different clusters comprise the final rank. Devising thresholds for filtering irrelevant and redundant features has facilitated the adaptability of our model to the particular needs of various applications. A comprehensive evaluation based on benchmark biological and image datasets shows the superiority of our proposed methods compared to the state-of-the-art FS methods in terms of classification accuracy, running time, and memory usage

Segmentation and Unsupervised Adversarial Domain Adaptation Between Medical Imaging Modalities

Segmenting and labelling tumors in multimodal medical imaging are often vital parts of diagnostics and can in many cases be very labor intensive for clinicians. The effort in advancing time-saving methods in the medical health sector might be of great help for busy clinicians and can maybe even save lives. Furthermore, creating methods that generically, accurately and successfully process unlabelled data would be a major breakthrough in deep learning. This thesis aims to address both these challenges by exploring and improving current methods involving adversarial discriminative domain adaptation (ADDA) on multimodal imaging, and address weaknesses, not only in ADDA, but also in the general adversarial discriminative cases. More specifically, this thesis - applies convolutional neural networks to segment soft tissue sarcomas in PET, CT and MRI modalities, and to the author's best knowledge achieves state-of-the-art results, - explores unsupervised adversarial discriminative domain adaptation on segmentation of soft tissue sarcoma tumors between permutations of PET, CT and MRI and - demonstrates weaknesses in state-of-the-art adversarial discriminative training, and finally - improves and provides groundwork for further research on said techniques. Additionally, the thesis will also provide strong fundamental background for applying ADDA for use in medical modalities, including a solid introduction to deep learning in medical imaging, both from a theoretical and practical aspect

Application of Machine Learning to Financial Time Series Analysis

This multidisciplinary thesis investigates the application of machine learning to financial time series analysis. The research is motivated by the following thesis question: ‘Can one improve upon the state of the art in financial time series analysis through the application of machine learning?’ The work is split according to the following time series trichotomy: 1) characterization — determine the fundamental properties of the time series; 2) modelling — find a description that accurately captures features of the long-term behaviour of the system; and 3) forecasting — accurately predict the short-term evolution of the system

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden