REDUS: finding reducible subspaces in high dimensional data

Finding latent patterns in high dimensional data is an important research problem with numerous applications. The most well known approaches for high dimensional data analysis are feature selection and dimensionality reduction. Being widely used in many applications, these methods aim to capture global patterns and are typically performed in the full feature space. In many emerging applications, however, scientists are interested in the local latent patterns held by feature subspaces, which may be invisible via any global transformation. In this paper, we investigate the problem of finding strong linear and nonlinear correlations hidden in feature subspaces of high dimensional data. We formalize this problem as identifying reducible subspaces in the full dimensional space. Intuitively, a reducible subspace is a feature subspace whose intrinsic dimensionality is smaller than the number of features. We present an effective algorithm, REDUS, for finding the reducible subspaces. Two key c...

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Xiang Zhang, Feng Pan, Wei Wang 0010
• Keywords: CIKM 2008, Feature Subspace, Information Management, Overall Reducible Subspace, Reducible Subspace