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 language||English (US)|
|Title of host publication||Link Mining|
|Subtitle of host publication||Models, Algorithms, and Applications|
|Publisher||Springer New York|
|Number of pages||30|
|State||Published - Jan 1 2010|
All Science Journal Classification (ASJC) codes