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Clustering of multimodal data according to their information content is considered in this paper. Statistical correlations present in data that contain similar information are exploited to perform the clustering task. Specifically, multiset canonical correlation analysis is equipped with norm-one regularization mechanisms to identify clusters within different types of data that share the same information...
A novel solution of the inverse Frobenius-Perron problem for constructing semi-Markov chaotic maps with prescribed statistical properties is presented. The proposed solution uses recursive Markov state disaggregation to construct an ergodic map with a piecewise constant invariant density function that approximates an arbitrary probability distribution over a compact interval. The solution is novel...
Brain functional connectivity measured by functional magnetic resonance imaging was shown to be influenced by preprocessing procedures. We aim to describe this influence separately for different preprocessing factors and in 20 different most used preprocessing pipelines. We evaluate the effects of slice-timing correction and physiological noise filtering by RETROICOR, diverse levels of motion correction,...
As for nonstationary signal, such as subpixel peak detection,we could be difficult to suppress the noise of super- Gaussian and sub-Gaussian in the mixed signal with the traditional low order filter. The gradient search method is generally adopt in the filter algorithm based on higher order statistics, but it is difficult to avoid local convergence and large complexity in the gradient search process...
This paper presents a detection scheme for determining the number of signals that are correlated across multiple data sets when the sample size is small compared to the dimensions of the data sets. To accommodate the sample-poor regime, we decouple the problem into several independent two-channel order-estimation problems that may be solved separately by a combination of principal component analysis...
In this paper, we deal with the classification of Greek folk songs into 8 classes associated with the region of origin of the songs. Motivated by the way the sound is perceived by the human auditory system, auditory cortical representations are extracted from the music recordings. Moreover, deep canonical correlation analysis (DCCA) is applied to the auditory cortical representations for dimensionality...
In this work we consider a two-channel passive detection problem, in which there is a surveillance array where the presence/absence of a target signal is to be detected, and a reference array that provides a noise-contaminated version of the target signal. We assume that the transmitted signal is an unknown rank-one signal, and that the noises are uncorrelated between the two channels, but each one...
In room acoustics, the under-modelled blind system identification (BSI) problem arises when the identified room impulse response (RIR) is shorter than the real one. Conventional BSI methods can perform poorly under these circumstances. In this paper, we propose an algorithm for multichannel BSI in under-modelled situations. Instead of minimizing the cross-relation error, a new optimization criterion...
Covariance sketching has been recently introduced as an effective strategy to reduce the data dimensionality without sacrificing the ability to reconstruct second-order statistics of the data. In this paper, we propose a novel covariance sketching scheme with reduced complexity for spatial-temporal data, whose covariance matrices satisfy the Kronecker product expansion model recently introduced by...
The autoregressive models (AR) and moving-average models (MA) are regularly used in signal processing. Previous works have been done on dissimilarity measures between AR models by using a Riemannian distance, the Jeffrey's divergence (JD) and the spectral distances such as the Itakura-Saito divergence. In this paper, we compare the Rao distance and the JD for MA models and more particularly in the...
Correlation size together with Lyapunov exponents estimated from both electroencephalography (EEG) and electromyography (EMG) signals, are the crucial variables in the classification of mental tasks using an artificial neural network (ANN) classifier for patients suffering from neurological disorders/diseases. The above parameters vary according to the status of the patient, for example: depending...
Sparse reconstruction algorithms aim to retrieve high-dimensional sparse signals from a limited number of measurements. A common example is LASSO or Basis Pursuit where sparsity is enforced using an ℓ1-penalty together with a cost function ‖y — Hx‖22. For random design matrices H, a sharp phase transition boundary separates the ‘good’ parameter region where error-free recovery of a sufficiently sparse...
In this paper we study the application of Matrix Completion in topic detection and classification in Twitter. The proposed method first employs Joint Complexity to perform topic detection based on score matrices. Based on the spatial correlation of tweets and the spatial characteristics of the score matrices, we apply a novel framework which extends the Matrix Completion to build dynamically complete...
In this paper some results on Schur transform are reviewed to address the problem of one-dimensional discrete phase retrieval. The goal is to provide a test whether a sequence of input magnitude data gives a solution to one-dimensional discrete phase retrieval problem. It has been previously shown that this issue is related to the nonnegativity of trigonometric polynomials. The proposed method is...
In this paper, we consider the problem of estimating an unknown random scalar observed by two modalities. We study two scenarios using mutual information and mean square error. In the first scenario, we consider that the noise correlation is known and examine its impact on the information content of two modalities. In the second scenario we quantify the information loss when the considered value of...
A periodic sequence is defined as a perfect periodic sequence for a certain nonlinear filter if the cross-correlation between any two of the filter basis functions, estimated over a period, is zero. Using a perfect periodic sequence as input signal, an unknown nonlinear system can be efficiently identified with the cross-correlation method. Moreover, the basis functions that guarantee the most compact...
In this paper, we propose to address the moving average (MA) parameters estimation issue based only on noisy observations and without any knowledge on the variance of the additive stationary white Gaussian measurement noise. For this purpose, the MA process is approximated by a high-order AR process and its parameters are estimated by using an errors-in-variables (EIV) approach, which also makes it...
Autoregressive (AR) models are used in various applications, from speech processing to radar signal analysis. In this paper, our purpose is to extract different model subsets from a set of two or more AR models. The approach operates with the following steps: firstly the matrix composed of dissimilarity measures between AR-model pairs are created. This can be based on the symmetric Itakura divergence,...
We extend the concept of stationary temporal signals to stationary graph signals. Doing so, we introduce the concept of strict sense stationary and wide sense stationary graph signals as a statistical invariance through an isometric graph translation operator. Using these definitions, we propose a spectral characterisation of WSS graph signals allowing to study stationarity using only the spectral...
The method of instrumental variables has been successfully applied to pseudolinear estimation for angle-of-arrival target motion analysis (TMA). The objective of instrumental variables is to modify the normal equations of a biased least-squares estimator to make it asymptotically unbiased. The instrumental variable (IV) matrix, used in the modified normal equations, is required to be strongly correlated...
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