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Related Experiment Videos

Robust Alternating Low-Rank Representation by joint Lp- and L2,p-norm minimization.

Zhao Zhang1, Mingbo Zhao2, Fanzhang Li1

  • 1School of Computer Science and Technology & Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou 215006, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 9, 2017
PubMed
Summary

We introduce an Alternating Low-Rank Representation (ALRR) model that integrates local Robust PCA (RPCA) and sparse low-rank representation (LRR) for robust data representation and outlier pursuit.

Keywords:
Adaptive local RPCAAlternating low-rank representationForward–backward representationJoint - and -norm minimizationOutlier pursuit

Related Experiment Videos

Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Robust Principal Component Analysis (RPCA) is crucial for separating low-rank structures from corruptions.
  • Sparse Low-Rank Representation (LRR) is effective for subspace recovery and feature learning.
  • Existing methods often struggle with noisy data and efficient handling of external datasets.

Purpose of the Study:

  • To develop a robust Alternating Low-Rank Representation (ALRR) model for enhanced data representation.
  • To improve outlier pursuit and subspace recovery in the presence of noise and corruptions.
  • To derive a projective ALRR for direct feature extraction from new data.

Main Methods:

  • ALRR employs an alternating forward-backward process, starting with adaptive local RPCA to recover low-rank components and corruptions.
  • Sparse LRR is performed using joint L_p-norm and L_{2,p}-norm minimization on coding coefficients, ensuring sparsity and handling reconstruction errors.
  • A novel iterative scheme based on the Iterative Shrinkage/Thresholding (IST) approach is presented to solve the minimization problem.

Main Results:

  • The proposed ALRR model integrates local RPCA with adaptive weights and sparse LRR with a self-expressive low-rank dictionary.
  • A projective ALRR variant is derived for efficient handling of outside data through direct feature extraction.
  • The developed iterative scheme effectively solves the L_{2,p}-norm minimization problem.

Conclusions:

  • ALRR offers a more general and robust approach to data representation compared to existing methods.
  • The model demonstrates effectiveness in handling noisy data and achieving accurate subspace recovery.
  • Visual and numerical results validate the superior performance of ALRR algorithms.