Nonlinear feature extraction through manifold learning in an electronic tongue classification task

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.Peer ReviewedPostprint (published version

Hyp-UML: Hyperbolic Image Retrieval with Uncertainty-aware Metric Learning

Metric learning plays a critical role in training image retrieval and classification. It is also a key algorithm in representation learning, e.g., for feature learning and its alignment in metric space. Hyperbolic embedding has been recently developed. Compared to the conventional Euclidean embedding in most of the previously developed models, Hyperbolic embedding can be more effective in representing the hierarchical data structure. Second, uncertainty estimation/measurement is a long-lasting challenge in artificial intelligence. Successful uncertainty estimation can improve a machine learning model's performance, robustness, and security. In Hyperbolic space, uncertainty measurement is at least with equivalent, if not more, critical importance. In this paper, we develop a Hyperbolic image embedding with uncertainty-aware metric learning for image retrieval. We call our method Hyp-UML: Hyperbolic Uncertainty-aware Metric Learning. Our contribution are threefold: we propose an image embedding algorithm based on Hyperbolic space, with their corresponding uncertainty value; we propose two types of uncertainty-aware metric learning, for the popular Contrastive learning and conventional margin-based metric learning, respectively. We perform extensive experimental validations to prove that the proposed algorithm can achieve state-of-the-art results among related methods. The comprehensive ablation study validates the effectiveness of each component of the proposed algorithm

Non-Parallel Articulatory-to-Acoustic Conversion Using Multiview-based Time Warping

This work was supported in part by the Spanish State Research Agency (SRA) grant number PID2019-108040RB-C22/SRA/10.13039/501100011033, and the FEDER/Junta de AndalucíaConsejería de Transformación Económica, Industria, Conocimiento y Universidades project no. B-SEJ-570-UGR20.In this paper, we propose a novel algorithm called multiview temporal alignment by dependence maximisation in the latent space (TRANSIENCE) for the alignment of time series consisting of sequences of feature vectors with different length and dimensionality of the feature vectors. The proposed algorithm, which is based on the theory of multiview learning, can be seen as an extension of the well-known dynamic time warping (DTW) algorithm but, as mentioned, it allows the sequences to have different dimensionalities. Our algorithm attempts to find an optimal temporal alignment between pairs of nonaligned sequences by first projecting their feature vectors into a common latent space where both views are maximally similar. To do this, powerful, nonlinear deep neural network (DNN) models are employed. Then, the resulting sequences of embedding vectors are aligned using DTW. Finally, the alignment paths obtained in the previous step are applied to the original sequences to align them. In the paper, we explore several variants of the algorithm that mainly differ in the way the DNNs are trained. We evaluated the proposed algorithm on a articulatory-to-acoustic (A2A) synthesis task involving the generation of audible speech from motion data captured from the lips and tongue of healthy speakers using a technique known as permanent magnet articulography (PMA). In this task, our algorithm is applied during the training stage to align pairs of nonaligned speech and PMA recordings that are later used to train DNNs able to synthesis speech from PMA data. Our results show the quality of speech generated in the nonaligned scenario is comparable to that obtained in the parallel scenario.Spanish State Research Agency (SRA) PID2019-108040RB-C22/SRA/10.13039/501100011033FEDER/Junta de AndalucíaConsejería de Transformación Económica, Industria, Conocimiento y Universidades project no. B-SEJ-570-UGR20

Non-linear dimensionality reduction techniques for classification

This thesis project concerns on dimensionality reduction through manifold learning with a focus on non linear techniques. Dimension Reduction (DR) is the process of reducing high dimension dataset with d feature (dimension) to one with a lower number of feature p (p ≪ d) that preserves the information contained in the original higher dimensional space. More in general, the concept of manifold learning is introduced, a generalized approach that involves algorithm for dimensionality reduction. Manifold learning can be divided in two main categories: Linear and Non Linear method. Although, linear method, such as Principal Component Analysis (PCA) and Multidimensional Scaling (MDS) are widely used and well known, there are plenty of non linear techniques i.e. Isometric Feature Mapping (Isomap), Locally Linear Embedding (LLE), Local Tangent Space Alignment (LTSA), which in recent years have been subject of studies. This project is inspired by the work done by [Bahadur et Al., 2017 ], with the aim to estimate the US market dimensionality using Russell 3000 as a proxy of financial market. Since financial markets are high dimensional and complex environment an approach with non linear techniques among linear is proposed.This thesis project concerns on dimensionality reduction through manifold learning with a focus on non linear techniques. Dimension Reduction (DR) is the process of reducing high dimension dataset with d feature (dimension) to one with a lower number of feature p (p ≪ d) that preserves the information contained in the original higher dimensional space. More in general, the concept of manifold learning is introduced, a generalized approach that involves algorithm for dimensionality reduction. Manifold learning can be divided in two main categories: Linear and Non Linear method. Although, linear method, such as Principal Component Analysis (PCA) and Multidimensional Scaling (MDS) are widely used and well known, there are plenty of non linear techniques i.e. Isometric Feature Mapping (Isomap), Locally Linear Embedding (LLE), Local Tangent Space Alignment (LTSA), which in recent years have been subject of studies. This project is inspired by the work done by [Bahadur et Al., 2017 ], with the aim to estimate the US market dimensionality using Russell 3000 as a proxy of financial market. Since financial markets are high dimensional and complex environment an approach with non linear techniques among linear is proposed

Kernels for Protein Homology Detection

Determining protein sequence similarity is an important task for protein classification and homology detection, which is typically performed using sequence alignment algorithms. Fast and accurate alignment-free kernel based classifiers exist, that treat protein sequences as a “bag of words”. Kernels implicitly map the sequences to a high dimensional feature space, and can be thought of as an inner product between two vectors in that space. This allows an algorithm that can be expressed purely in terms of inner products to be ‘kernelised’, where the algorithm implicitly operates in the kernel’s feature space. A weighted string kernel, where the weighting is derived using probabilistic methods, is implemented using a binary data representation, and the results reported. Alternative forms of data representation, such as Ising and frequency forms, are implemented and the results discussed. These results are then used to inform the development of a variety of novel kernels for protein sequence comparison. Alternative forms of classifier are investigated, such as nearest neighbour, support vector machines, and multiple kernel learning. A kernelized Gaussian classifier is derived and tested, which is informative as it returns a score related to the probability of a sequence belonging to a particular classification. Support vector machines are tested with the introduced kernels, and the results compared to alternate classifiers. As similarity can be thought of as having different components, such as composition and position, multiple kernel learning is investigated with the novel kernels developed here. The results show that a support vector machine, using either single or multiple kernels, is the best classifier for remote protein homology detection out of all the classifiers tested in this thesis.EPSR

Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions

Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment errors. We propose tu use a mixture of linear regressions with partially-latent output. This regression method learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of head-pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena. We describe in detail the mapping method that combines the merits of unsupervised manifold learning techniques and of mixtures of regressions. We validate our method with three publicly available datasets and we thoroughly benchmark four variants of the proposed algorithm with several state-of-the-art head-pose estimation methods.Comment: 12 pages, 5 figures, 3 table

Non-parallel articulatory-to-acoustic conversion using multiview-based time warping

In this paper, we propose a novel algorithm called multiview temporal alignment by dependence maximisation in the latent space (TRANSIENCE) for the alignment of time series consisting of sequences of feature vectors with different length and dimensionality of the feature vectors. The proposed algorithm, which is based on the theory of multiview learning, can be seen as an extension of the well-known dynamic time warping (DTW) algorithm but, as mentioned, it allows the sequences to have different dimensionalities. Our algorithm attempts to find an optimal temporal alignment between pairs of nonaligned sequences by first projecting their feature vectors into a common latent space where both views are maximally similar. To do this, powerful, nonlinear deep neural network (DNN) models are employed. Then, the resulting sequences of embedding vectors are aligned using DTW. Finally, the alignment paths obtained in the previous step are applied to the original sequences to align them. In the paper, we explore several variants of the algorithm that mainly differ in the way the DNNs are trained. We evaluated the proposed algorithm on a articulatory-to-acoustic (A2A) synthesis task involving the generation of audible speech from motion data captured from the lips and tongue of healthy speakers using a technique known as permanent magnet articulography (PMA). In this task, our algorithm is applied during the training stage to align pairs of nonaligned speech and PMA recordings that are later used to train DNNs able to synthesis speech from PMA data. Our results show the quality of speech generated in the nonaligned scenario is comparable to that obtained in the parallel scenario

Temporal Feature Alignment in Contrastive Self-Supervised Learning for Human Activity Recognition

Automated Human Activity Recognition has long been a problem of great interest in human-centered and ubiquitous computing. In the last years, a plethora of supervised learning algorithms based on deep neural networks has been suggested to address this problem using various modalities. While every modality has its own limitations, there is one common challenge. Namely, supervised learning requires vast amounts of annotated data which is practically hard to collect. In this paper, we benefit from the self-supervised learning paradigm (SSL) that is typically used to learn deep feature representations from unlabeled data. Moreover, we upgrade a contrastive SSL framework, namely SimCLR, widely used in various applications by introducing a temporal feature alignment procedure for Human Activity Recognition. Specifically, we propose integrating a dynamic time warping (DTW) algorithm in a latent space to force features to be aligned in a temporal dimension. Extensive experiments have been conducted for the unimodal scenario with inertial modality as well as in multimodal settings using inertial and skeleton data. According to the obtained results, the proposed approach has a great potential in learning robust feature representations compared to the recent SSL baselines, and clearly outperforms supervised models in semi-supervised learning. The code for the unimodal case is available via the following link: https://github.com/bulatkh/csshar_tfa.Comment: Accepted to IJCB 202