Finding high-order correlations in high-dimensional biological data

Xiang Zhang, Feng Pan, Wei Wang

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Scopus citations


In many emerging real-life problems, the number of dimensions in the data sets can be from thousands to millions. The large number of features poses great challenge to existing high-dimensional data analysis methods. One particular issue is that the latent patterns may only exist in subspaces of the full-dimensional space. In this chapter, we discuss the problem of finding correlations hidden in feature subspaces. Both linear and nonlinear cases will be discussed. We present efficient algorithms for finding such correlated feature subsets.

Original languageEnglish (US)
Title of host publicationLink Mining
Subtitle of host publicationModels, Algorithms, and Applications
PublisherSpringer New York
Number of pages30
ISBN (Electronic)9781441965158
ISBN (Print)9781441965141
StatePublished - 2010

All Science Journal Classification (ASJC) codes

  • General Medicine


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