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A tensor approximation approach to dimensionality reduction
Hongcheng Wang,
Narendra Ahuja
National Center for Supercomputing Applications (NCSA)
Electrical and Computer Engineering
Siebel School of Computing and Data Science
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Keyphrases
Dimensionality Reduction
100%
Approximation Approach
100%
Tensor Approximation
100%
Image Ensembles
75%
A-Si
50%
Approximation Algorithms
50%
Pattern Recognition
50%
Rank-one Decomposition
50%
Sequence Data
25%
Data Matrix
25%
Compression Ratio
25%
Computer Vision
25%
2D Image
25%
Multidimensional Data
25%
High Dimension
25%
Least Squares
25%
International Conference
25%
Temporal Redundancy
25%
Matrix Representation
25%
Video Sequences
25%
Vision Recognition
25%
Vector Method
25%
Object Classification
25%
Computer Vision Applications
25%
Higher-order Tensor
25%
Volume Data
25%
Fewer Iterations
25%
Matrix Method
25%
Matrix Decomposition
25%
IEEE Computer Society
25%
Video Volume
25%
Third-order Tensor
25%
Compact Data Representation
25%
Multilinear Algebra
25%
Generalized Low Rank Approximations of Matrices
25%
Computer Science
Approximation (Algorithm)
100%
Dimensionality Reduction
100%
Approximation Algorithms
66%
Pattern Recognition
66%
Computer Vision
33%
Matrix Representation
33%
Compression Ratio
33%
Least Squares Method
33%
Data Representation
33%
Multidimensional Data
33%
Data Matrix
33%
Rank Approximation
33%
Information Loss
33%
Video Sequences
33%
Computer Vision Applications
33%
Mathematics
Tensor
100%
Dimensionality Reduction
100%
Matrix (Mathematics)
37%
Pattern Recognition
25%
Matrix Representation
12%
Least Square
12%
Compression Ratio
12%
Approximates
12%
Higher Dimensions
12%
Principal Component Analysis
12%
Matrix Method
12%
Main Result
12%
Low-Rank Approximation
12%
Square Sense
12%
Multilinear Algebra
12%