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» REDUS: finding reducible subspaces in high dimensional data
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CIKM
2008
Springer
9 years 2 months ago
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...
Xiang Zhang, Feng Pan, Wei Wang 0010
CVPR
2004
IEEE
10 years 2 months ago
Inference of Multiple Subspaces from High-Dimensional Data and Application to Multibody Grouping
Multibody grouping is a representative of applying subspace constraints in computer vision tasks. Under linear projection models, feature points of multibody reside in multiple su...
Zhimin Fan, Jie Zhou, Ying Wu
SIGMOD
2000
ACM
165views Database» more  SIGMOD 2000»
9 years 4 months ago
Finding Generalized Projected Clusters In High Dimensional Spaces
High dimensional data has always been a challenge for clustering algorithms because of the inherent sparsity of the points. Recent research results indicate that in high dimension...
Charu C. Aggarwal, Philip S. Yu
SIGMOD
2002
ACM
246views Database» more  SIGMOD 2002»
10 years 5 days ago
Hierarchical subspace sampling: a unified framework for high dimensional data reduction, selectivity estimation and nearest neig
With the increased abilities for automated data collection made possible by modern technology, the typical sizes of data collections have continued to grow in recent years. In suc...
Charu C. Aggarwal
ICDE
2008
IEEE
158views Database» more  ICDE 2008»
10 years 1 months ago
CARE: Finding Local Linear Correlations in High Dimensional Data
Finding latent patterns in high dimensional data is an important research problem with numerous applications. Existing approaches can be summarized into 3 categories: feature selec...
Xiang Zhang, Feng Pan, Wei Wang
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