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Multi-Instance Dimensionality Reduction via Sparsity and Orthogonality
Hong Zhu, Li Zhi Liao,
Michael K. Ng
Department of Mathematics
Research output
:
Contribution to journal
›
Letter
›
peer-review
2
Citations (Scopus)
51
Downloads (Pure)
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Dive into the research topics of 'Multi-Instance Dimensionality Reduction via Sparsity and Orthogonality'. Together they form a unique fingerprint.
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Keyphrases
Dimensionality Reduction
100%
Sparsity
100%
Orthogonality
100%
Multi-instance
100%
Optimization Problem
40%
Main Idea
20%
Learning Algorithm
20%
Iterative Algorithm
20%
Novel Algorithm
20%
Global Convergence
20%
Augmented Lagrangian Method
20%
Learning Performance
20%
Multiple Instance Learning
20%
Proximal Alternating Linearized Minimization
20%
Instance Data
20%
Orthogonality Constraint
20%
High Sparsity
20%
Outer Loop
20%
Sparsity Constraint
20%
Engineering
Dimensionality
100%
Sparsity
100%
Orthogonality
100%
Optimisation Problem
33%
Experimental Result
16%
Main Idea
16%
Learning Algorithm
16%
Iterative Algorithm
16%
Main Advantage
16%
Objective Function
16%
Real Data
16%
Outer Loop
16%
Computer Science
Dimensionality Reduction
100%
Sparsity
100%
Optimization Problem
40%
Experimental Result
20%
Learning Algorithm
20%
Real Data Sets
20%
Reduction Algorithm
20%
Objective Function
20%
Learning Performance
20%
Instance Learning
20%
Instance Data
20%
Iterative Algorithm
20%
Global Convergence
20%