Matrix Factorization for Signal Processing and Machine Learning
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 6120
Special Issue Editors
Interests: signal processing; machine learning; neural networks; wireless communications
Special Issues, Collections and Topics in MDPI journals
Interests: communication theory; signal detection; sequence design; lattices; coding theory
Special Issue Information
Dear Colleagues,
Compressed sensing is a recent sampling method proposed by Candes and Donoho in 2006. It is an alternative to Shannon/Nyquist sampling for the acquisition of sparse or compressible signals. This paradigm immediately aroused a wide range of research activities in the signal processing and machine learning communities, and many new research topics have been proposed. The research topics are generally dealt with by solving a matrix factorization problem subject to some form of an L-norm constraint.
This Special Issue will focus on recent theoretical and application studies of matrix factorization, with a focus on signal processing, image processing, machine learning, data mining, and knowledge discovery. Topics include, but are not limited to, the following:
- Singular value decomposition;
- Factor analysis;
- Principal component analysis;
- Independent component analysis;
- Blind source separation;
- Clustering based on matrix operation;
- Compressed sensing;
- Sparse recovery;
- Sparse coding and dictionary learning;
- Matrix completion;
- Matrix decomposition;
- Low-rank representation;
- Matrix approximation;
- Nonnegative matrix factorization;
- Concept factorization;
- CX decomposition;
- CUR decomposition;
- Latent semantic indexing;
- Theoretical analysis of related methods;
- Application of related methods.
Dr. Ke-Lin Du
Prof. Dr. Wai Ho Mow
Prof. Dr. M. N. S. Swamy
Guest Editors
Manuscript Submission Information
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Keywords
- matrix factorization
- matrix decomposition
- matrix approximation
- sparse approximation
- compressed sensing
- dictionary learning
- Nyström method
- clustering
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