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» Computing the Dimension of Linear Subspaces
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CVPR
2004
IEEE
14 years 8 months ago
Minimum Effective Dimension for Mixtures of Subspaces: A Robust GPCA Algorithm and Its Applications
In this paper, we propose a robust model selection criterion for mixtures of subspaces called minimum effective dimension (MED). Previous information-theoretic model selection cri...
Kun Huang, René Vidal, Yi Ma
CGF
2011
12 years 9 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
DCG
2006
110views more  DCG 2006»
13 years 6 months ago
High-Dimensional Centrally Symmetric Polytopes with Neighborliness Proportional to Dimension
Let A be a d by n matrix, d < n. Let C be the regular cross polytope (octahedron) in Rn . It has recently been shown that properties of the centrosymmetric polytope P = AC are ...
David L. Donoho
CVPR
2005
IEEE
14 years 8 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
ICPP
2000
IEEE
13 years 10 months ago
A Scalable Parallel Subspace Clustering Algorithm for Massive Data Sets
Clustering is a data mining problem which finds dense regions in a sparse multi-dimensional data set. The attribute values and ranges of these regions characterize the clusters. ...
Harsha S. Nagesh, Sanjay Goil, Alok N. Choudhary