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» REDUS: finding reducible subspaces in high dimensional data
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COLT
2010
Springer
8 years 10 months ago
Principal Component Analysis with Contaminated Data: The High Dimensional Case
We consider the dimensionality-reduction problem (finding a subspace approximation of observed data) for contaminated data in the high dimensional regime, where the number of obse...
Huan Xu, Constantine Caramanis, Shie Mannor
SDM
2009
SIAM
205views Data Mining» more  SDM 2009»
9 years 9 months ago
Identifying Information-Rich Subspace Trends in High-Dimensional Data.
Identifying information-rich subsets in high-dimensional spaces and representing them as order revealing patterns (or trends) is an important and challenging research problem in m...
Chandan K. Reddy, Snehal Pokharkar
CVPR
2005
IEEE
10 years 2 months ago
Subspace Analysis Using Random Mixture Models
In [1], three popular subspace face recognition methods, PCA, Bayes, and LDA were analyzed under the same framework and an unified subspace analysis was proposed. However, since t...
Xiaogang Wang, Xiaoou Tang
CGF
2011
8 years 3 months ago
Visualizing High-Dimensional Structures by Dimension Ordering and Filtering using Subspace Analysis
High-dimensional data visualization is receiving increasing interest because of the growing abundance of highdimensional datasets. To understand such datasets, visualization of th...
Bilkis J. Ferdosi, Jos B. T. M. Roerdink
CVPR
2007
IEEE
10 years 2 months ago
Approximate Nearest Subspace Search with Applications to Pattern Recognition
Linear and affine subspaces are commonly used to describe appearance of objects under different lighting, viewpoint, articulation, and identity. A natural problem arising from the...
Ronen Basri, Tal Hassner, Lihi Zelnik-Manor
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