Robust Subspace Tracking

Subspace tracking aims to address the problem when new observations come in asynchronously in the case of online streaming application. The algorithm cannot in general store all the input data in memory. The incoming observations must be immediate processed and then discarded. Furthermore, since the subspace can be identified from incomplete vectors, it can be subsampled in order to improve on computational efficiency, and it still retain subspace estimation accuracy. The involved subspaces can have low-rank and/or sparse structures as in the other decomposition frameworks

1. Robust Subspace Tracking (23 papers)

• Grassmannian Rank-One Update Subspace Estimation (GROUSE) (1 paper)

• Grassmannian Robust Adaptive Subspace Tracking Algorithm (GRASTA) (5 papers)

• lp-norm Robust Online Subspace Tracking (pROST) (2 papers)

• Grassmannian Online Subspace Updates with Structured-sparsity (GOSUS) (1 paper)

• Fast Adaptive Robust Subspace Tracking (FARST) (1 paper)

• Robust Online Subspace Estimation and Tracking Algorithm (ROSETA) (2 papers)

• Robust Subspace Tracking via Adaptive Projected Subgradient Method (APSM) (2 papers)

• Riemannian Optimization Method with Subspace Tracking (ROAM) (1 paper)

• Subspace Tracking under Dynamic Dimensionality (1 paper)

• Nearly Optimal Robust Subspace Tracking (NORST) (7 papers)

2. Robust Mulit-Subspace Tracking (4 papers)

• Multiscale Online Union of SubSpaces Estimation (1 paper)

• Online Data Thinning via Multi-Subspace Tracking (OT) (2 papers)

• Matrix Factorization with Column Constraints (1 paper)

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

T. Bouwmans . A. Sobral, S. Javed, S. Jung, E. Zahzah, "Background/Foreground Separation via Decomposition in Low-rank and Additive Matrices: A Review for a Comparative Evaluation with a Large-Scale Dataset", to be submitted.

T. Bouwmans, E. Zahzah, “Robust PCA via Principal Component Pursuit: A Review for a Comparative Evaluation in Video Surveillance”, Special Issue on Background Models Challenge, Computer Vision and Image Understanding, CVIU 2014, Volume 122, pages 22–34, May 2014. [pdf]

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