Document Detail

REDUS: Finding Reducible Subspaces in High Dimensional Data.
MedLine Citation:
PMID:  20689667     Owner:  NLM     Status:  Publisher    
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 components of our algorithm are finding the overall reducible subspace, and uncovering the individual reducible subspaces from the overall reducible subspace. A broad experimental evaluation demonstrates the effectiveness of our algorithm.
Xiang Zhang; Feng Pan; Wei Wang
Related Documents :
19029547 - Bayesian clustering of fuzzy feature vectors using a quasi-likelihood approach.
9990627 - A fuzzy logic-controlled classifier for use in implantable cardioverter defibrillators.
12804087 - Estimating dataset size requirements for classifying dna microarray data.
16684357 - Regularized binormal roc method in disease classification using microarray data.
9874907 - Dynamic systems theory as a paradigm for the study of change in psychotherapy: an appli...
1742437 - Algorithms versus models for analyzing data that contain misclassification errors.
Publication Detail:
Journal Detail:
Title:  Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management     Volume:  2008     ISSN:  2155-0751     ISO Abbreviation:  -     Publication Date:  2008  
Date Detail:
Created Date:  2010-8-6     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101523541     Medline TA:  Proc ACM Int Conf Inf Knowl Manag     Country:  -    
Other Details:
Languages:  ENG     Pagination:  961-970     Citation Subset:  -    
Department of Computer Science University of North Carolina at Chapel Hill Chapel Hill, NC 27599, USA.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Grant Support
U01 CA105417-05//NCI NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Previous Document:  Comparing Object Alignment Algorithms with Appearance Variation: Forward-Additive vs Inverse-Composi...
Next Document:  Differential effects of prolonged work on performance measures in self-paced speed tests.