Tensor Decomposition for Machine Learning and Signal Processing
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (31 March 2019) | Viewed by 1187
Special Issue Editors
Interests: computer vision; image processing; pattern recognition; machine learning; deep learning
Interests: background subtraction; target detection; moving object detection; LBP features
Special Issues, Collections and Topics in MDPI journals
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
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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
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