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Related Concept Videos

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Related Experiment Video

Updated: Jun 16, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Biclustering via sparse singular value decomposition.

Mihee Lee1, Haipeng Shen, Jianhua Z Huang

  • 1Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.

Biometrics
|February 19, 2010
PubMed
Summary
This summary is machine-generated.

Sparse Singular Value Decomposition (SSVD) offers a novel biclustering approach for high-dimensional data. This method identifies interpretable row-column associations by creating sparse singular vectors for robust data analysis.

Related Experiment Videos

Last Updated: Jun 16, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

  • Data Science
  • Bioinformatics
  • Machine Learning

Background:

  • High-dimensional data analysis often requires methods to uncover complex patterns.
  • Biclustering aims to identify subgroups of rows and columns exhibiting similar behavior.

Purpose of the Study:

  • To introduce Sparse Singular Value Decomposition (SSVD) as a new tool for biclustering.
  • To identify interpretable row-column associations in high-dimensional datasets.

Main Methods:

  • SSVD approximates data matrices with a low-rank, checkerboard structure.
  • Sparsity is induced in singular vectors via regularization penalties in regression.
  • An efficient iterative algorithm is proposed for computing sparse singular vectors.

Main Results:

  • SSVD effectively performs biclustering on biological and nutritional datasets.
  • The method demonstrates competitive performance against existing biclustering techniques in simulations.

Conclusions:

  • SSVD is a valuable exploratory analysis tool for biclustering.
  • The sparsity-inducing approach enhances the interpretability of identified row-column associations.