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Hybrid Manifold Embedding
Presenter: HONG, CHIA-TSE
Authors:Yang Liu, Yan Liu, Keith C. C. Chan, Kien A. Hua
2014. TONNAL.
Intelligent Database Systems Lab
Outlines
 Motivation
 Objectives
 Methodology
 Experiments
 Conclusions
 Comments
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Motivation
• Most of the existing supervised manifold learning
algorithms that give linear explicit mapping function.
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Objectives
• This study present a novel supervised manifold
learning framework dubbed hybrid manifold
embedding.
• HyME aims to provide a more general nonlinear
explicit mapping function by performing a tow-layer
learning procedure.
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Methodology
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Methodology
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Methodology
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Intelligent Database Systems Lab
Methodology
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Intelligent Database Systems Lab
Methodology
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Methodology
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Methodology
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Experiment
LP
GC+LCDP
K-means+
LCDP
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Experiment
• USPS Digit Data Set
PCA
SOLPP
LDA
LPP
HyME, c=1
HyME, c=2
LPMIP
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Experiment
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Experiment
• HyME, C=2
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Experiment
GC+LCDP
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Experiment
• K=3,4,5時
績效很類似
• C>2績效下
降
K=2
K=3
K=4
K=5
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Experiment
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Conclusions
• HyME provides a nonlinear explicit mapping function by
performing a two-layer learning procedure.
• In the experiments, HyME get a good performance.
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Comments
• Advantages
- Providing a more general nonlinear explicit mapping
function
Intelligent Database Systems Lab