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We consider the problem of estimating the singular vectors of low-rank signal matrices buried in noise in the setting where the singular vectors are assumed to be Kronecker products of unknown vectors. We propose four algorithms for estimating such singular vectors, analyze their performance and show that they asymptotically fail to estimate to latent singular vector below the same critical SNR. We...
A new noninvasive nearfield electromagnetic imaging (EMI) system for highly coherent and compressively sensed (CS) data at only few sensing positions is presented in this paper. Principal component analysis (PCA) in combination with spatial CS and background subtraction is implemented for the enhanced imaging of highly dispersive and coherent target space. The proposed imaging system is applied by...
This paper investigates the application of the probabilistic linear discriminant analysis (PLDA) to speaker diarization of telephone conversations. We introduce using a variational Bayes (VB) approach for inference under a PLDA model for modelling segmental i-vectors in speaker diarization. Deterministic annealing (DA) algorithm is imposed in order to avoid local optimal solutions in VB iterations...
This paper addresses the problem of learning a collection of nonlinear manifolds. Inspired by kernel methods, it puts forth a generalization of the kernel subspace model, termed the Metric-Constrained Kernel Union-of-Subspaces (MC-KUoS) model. It then develops an iterative method for learning of an MC-KUoS whose solution is based on the data representation capability of the manifolds and distances...
This paper is concerned with determining the number of correlated signals between two data sets using canonical correlation analysis (CCA) when a principal component analysis (PCA) preprocessing step is performed for initial rank reduction. In signal processing applications, it is commonplace in scenarios with large dimensions, insufficient samples, or knowledge of low-rank underlying signals to extract...
It has been previously shown that, when both acoustic and articulatory training data are available, it is possible to improve phonetic recognition accuracy by learning acoustic features from this multi-view data with canonical correlation analysis (CCA). In contrast with previous work based on linear or kernel CCA, we use the recently proposed deep CCA, where the functional form of the feature mapping...
Very often data we encounter in practice is a collection of matrices rather than a single matrix. These multi-block data often share some common features, due to the background in which they are measured. In this study we propose a new concept of linked blind source separation (BSS) that aims at discovering and extracting unique and physically meaningful common components from multi-block data, which...
Variation of patterns in signal can be represented by the covariance structure of vectors or its eigensubspace. When information of the pattern variation is available, representation by the covariance matrix or the eigensubspace is useful for feature extraction and classification compared with standard vector or matrix representations.
Recent work has demonstrated the feasibility of extracting semantic categories directly from cortical measures (e.g., electroencephalography, EEG) during receptive tasks. Here, we automatically classify speech stimuli as either synonymous or non-synonymous with a prior prime in a speech-receptive task given only EEG data with up to 86.84% accuracy. An analysis of variance reveals no significant difference...
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