Special Issue "Tensor Decomposition for Machine Learning and Signal Processing"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 31 March 2019

Special Issue Editors

Guest Editor
Dr. Andrews Cordolino Sobral

Machine Learning Team of ActiveEon at Station-F, Paris office, 06560 Sophia Antipolis, France
Website | E-Mail
Interests: computer vision; image processing; pattern recognition; machine learning; deep learning
Guest Editor
Prof. Dr. Thierry Bouwmans

Maitre de Conférences, Laboratoire MIA, Université de La Rochelle, 17000 La Rochelle, France
Website | E-Mail
Interests: background subtraction; background modeling; foreground detection; target detection, fuzzy theory; dempster-shafer theory; robust PCA; deep learning models

Special Issue Information

Dear Colleagues,

Tensors generalize matrices to multiple dimensions, and consist of multidimensional arrays of numerical values. In the literature, tensors were first developed for psychometrics and chemometrics in the 20th century, and they have, since then, been employed in numerous other fields, including machine learning, signal processing and computer vision. In practice, tensors and their decompositions are suitable for unsupervised learning settings, but have recently gained interest for the mathematical modeling of deep neural networks and their applications in deep learning. However, in computer vision applications, tensors present the advantage of keeping the spatial and temporal properties of pixels in video sequences. Practically, several tensors decompositions, such as Canonical Polyadic Decomposition (CPD), Parallel Factor Analysis (PARAFAC), Tucker decomposition, Higher-Order Singular Value Decomposition (HOSVD), and Multilinear Singular Value Decomposition (MLSVD) have been developed over time, but it is sometimes challenging to employ them with current machine learning requirements regarding big data. Indeed, tensor decompositions are more and more required, both for machine learning and applications like signal processing and computer vision with related constraints, such as multi-dimensional big data and real-time computations.

The aim of this Special Issue is to seek new and innovative approaches and applications for tensor decompositions in machine learning. The topics of interest include, but are not limited to, the following areas:

  • Tensor decomposition algorithms
  • Temporal data
  • Multi-relational data
  • Clustering
  • Dictionary learning
  • Dimensionality reduction
  • Subspace learning
  • Latent variable modeling
  • Deep learning
  • Low-level feature design
  • Signal processing applications (speech, audio, communications, radar, etc.)
  • Computer vision applications (background/foreground separation, etc.)
  • Software libraries
  • Real-time implementation

Dr. Andrews Cordolino Sobral
Prof. Dr. Thierry Bouwmans
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Tensor decomposition algorithms
  • Temporal data
  • Multi-relational data
  • Clustering
  • Dictionnary learning
  • Dimensionality reduction
  • Subspace learning
  • Latent variable modeling
  • Deep learning
  • Low-level feature design
  • Software libraries
  • Real-time implementation

Published Papers

This special issue is now open for submission.
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