Overview

Author: Thierry BOUWMANS, Associate Professor, Lab. MIA, Univ.  Rochelle, France.

Further Improvements

If you would like to list your publication related to this topic on this website, please send me your publication in .pdf and I will add the reference.

Fair Use Policy

As this website gives many information that come from my research, please cite my following survey papers:

N. Vaswani, T. Bouwmans, S. Javed, P. Narayanamurthy, “Robust Subspace Learning: Robust PCA, Robust Subspace Tracking and Robust Subspace Recovery”, IEEE Signal Processing Magazine, Volume 35, No. 4, pages 32-55, July 2018. [pdf]

N. Vaswani, T. Bouwmans, S. Javed, P. Narayanamurthy, “Robust PCA and Robust Subspace Tracking: A Comparative Evaluation”, IEEE Statistical Signal Processing Workshop, SSP 2018, Freiburg, Germany, June 2018.

T. Bouwmans, S. Javed, H. Zhang, Z. Lin, R. Otazo, “On the Applications of Robust PCA in Image and Video Processing”, Special Issue on "Rethinking PCA for Modern Datasets: Theory, Algorithms, and Applications”, Proceedings of IEEE, July 2018.

Objective

The aim of this web site is to provide ressources such as references (2286 papers), codes and links to demonstration websites  for the research on subspace learning by grouping all related researches and particularly recent advances in this field.  For this, it  is organized in the following sections:

1. Challenges (6 papers)

2. Different Subspace Learning Frameworks (4 papers)

3. Reconstructive Subspace Learning (2159 papers)

3.1 Conventional  Reconstructive Subspace Learning (2117 papers)

Principal Components Analysis (2043 papers), Independent Components Analysis (29 papers), Non-negative Matrix Factorization (42 papers), Minimum Trace Factor Analysis (3 papers)

3.2 Recent  Reconstructive Subspace Learning (43 papers)

Isometric Feature Mapping (ISOMAP) (2 papers), Locally Linear Embedding (3 papers),Laplacian Eigenmaps (1 paper), Hessian Eigenmaps (HE) (1 paper), Diffusion Maps (DM) (2 papers), Diffusion Bases (DB) (1 paper), Local Tangent Space Alignment (1 paper), Locality Preserving Projections (13 papers),Neighborhood Preserving Embedding (7 papers),Stationary Subspace Analysis (2 papers), Signal Subspace Matching (4 papers)

4. Discriminative Subspace Learning  (90 papers)

4.1 Conventional  Discriminative Subspace Learning

Linear Discriminant Analysis (LDA) (35 papers),Canonical Correlation Analysis (CCA) (46 papers), Maximum Margin Criterion (MMC) (6 papers)

4.2 Recent  Discriminative Subspace Learning

Quadratic Discriminant Analysis (QDA) (0 paper), Regularized Discriminant Analysis (RDA) (3 papers)

5. Mixed Subspace Learning (6 papers)

Mixed PCA/LDA (6 papers)

6. Other Subspace Learning (16 papers)

Multi-View Subspace Learning (3 papers),  Multilinear Subspace Learning (6 papers), Regularized Subspace Learning (3 papers), Graph Embedding (4 papers)

7. Surveys (4 papers)

PCA (1 paper),  Graph Embedding (3 papers)