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Orthogonal learning network for constrained principal component problem
S. Y. Kung
Electrical and Computer Engineering
Center for Statistics & Machine Learning
Research output
:
Contribution to conference
›
Paper
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peer-review
3
Scopus citations
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Dive into the research topics of 'Orthogonal learning network for constrained principal component problem'. Together they form a unique fingerprint.
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Keyphrases
Learning Networks
100%
Orthogonal Learning
100%
Numerical Analysis
66%
Principal Coordinate Analysis (PCoA)
66%
High-resolution
33%
Fast Convergence
33%
Stochastic Processes
33%
Spectral Analysis
33%
Still Images
33%
Anti-jamming
33%
Convergency
33%
Noisy-OR
33%
Theoretical Proof
33%
Image Data Compression
33%
Original Signals
33%
Constrained Principal Component Analysis
33%
Redundant Components
33%
Representative Components
33%
Optimal Learning Rate
33%
Motion Image
33%
Computer Science
Learning Network
100%
Principal Components
100%
Component Analysis
33%
Fast Convergence
11%
Data Compression
11%
Convergence Speed
11%
Learning Rate
11%
Engineering
Principal Components
100%
Component Analysis
33%
High Resolution
11%
Image Data
11%
Convergence Speed
11%
Convergency
11%
Economics, Econometrics and Finance
Principal Components
100%
Material Science
Nuclear Magnetic Resonance
100%