Bi Sparsity Pursuit: A Paradigm for Robust Subspace Recovery

Abstract

The success of sparse models in computer vision and machine learning is due to the fact that, high dimensional datais distributed in a union of low dimensional subspaces in many real-world applications. The underlying structuremay, however, be adversely affected by sparse errors. In this paper, we propose a bi-sparse model as a framework toanalyze this problem, and provide a novel algorithm to recover the union of subspaces in presence of sparsecorruptions. We further show the effectiveness of our method in a number of applications using real-world visiondata.

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Document Details

Document Type
Technical Report
Publication Date
Sep 27, 2016
Accession Number
AD1023874

Entities

People

  • Hamid Krim
  • Xiao Bian

Organizations

  • North Carolina State University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Cartesian Coordinates
  • Clustering
  • Coefficients
  • Computer Vision
  • Computers
  • Convex Programming
  • Coordinate Systems
  • Data Mining
  • Data Sets
  • Detection
  • Electronic Mail
  • Machine Learning
  • North Carolina
  • Optimization
  • Signal Processing
  • Sparse Matrix

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms