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Found 8 papers, 0 papers with code
Block-Term Tensor Decomposition Model Selection and Computation: The Bayesian Way
The so-called block-term decomposition (BTD) tensor model, especially in its rank-$(L_r, L_r, 1)$ version, has been recently receiving increasing attention due to its enhanced ability of representing systems and signals that are composed of \emph{blocks} of rank higher than one, a scenario encountered in numerous and diverse applications.
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization
Nowadays, the availability of large-scale data in disparate application domains urges the deployment of sophisticated tools for extracting valuable knowledge out of this huge bulk of information.
Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images
Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process.
Online Low-Rank Subspace Learning from Incomplete Data: A Bayesian View
Extracting the underlying low-dimensional space where high-dimensional signals often reside has long been at the center of numerous algorithms in the signal processing and machine learning literature during the past few decades.
Sparsity-aware Possibilistic Clustering Algorithms
The first one, called sparse possibilistic c-means, exploits sparsity and can deal well with closely located clusters that may also be of significantly different densities.
On the convergence of the sparse possibilistic c-means algorithm
In this paper, a convergence proof for the recently proposed sparse possibilistic c-means (SPCM) algorithm is provided, utilizing the celebrated Zangwill convergence theorem.
A Novel Adaptive Possibilistic Clustering Algorithm
Provided that the algorithm starts with a reasonable overestimate of the number of physical clusters formed by the data, it is capable, in principle, to unravel them (a long-standing issue in the clustering literature).
A variational Bayes framework for sparse adaptive estimation
Recently, a number of mostly $\ell_1$-norm regularized least squares type deterministic algorithms have been proposed to address the problem of \emph{sparse} adaptive signal estimation and system identification.
