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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 1 Intelligent Database Systems Lab Motivation • Most of the existing supervised manifold learning algorithms that give linear explicit mapping function. 2 Intelligent Database Systems Lab 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. 3 Intelligent Database Systems Lab Methodology 4 Intelligent Database Systems Lab Methodology 5 Intelligent Database Systems Lab Methodology 6 Intelligent Database Systems Lab Methodology 7 Intelligent Database Systems Lab Methodology 8 Intelligent Database Systems Lab Methodology 9 Intelligent Database Systems Lab Methodology 10 Intelligent Database Systems Lab Experiment LP GC+LCDP K-means+ LCDP 11 Intelligent Database Systems Lab Experiment • USPS Digit Data Set PCA SOLPP LDA LPP HyME, c=1 HyME, c=2 LPMIP 12 Intelligent Database Systems Lab Experiment 13 Intelligent Database Systems Lab Experiment • HyME, C=2 14 Intelligent Database Systems Lab Experiment GC+LCDP 15 Intelligent Database Systems Lab 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 16 Intelligent Database Systems Lab Experiment 17 Intelligent Database Systems Lab Conclusions • HyME provides a nonlinear explicit mapping function by performing a two-layer learning procedure. • In the experiments, HyME get a good performance. Intelligent Database Systems Lab Comments • Advantages - Providing a more general nonlinear explicit mapping function Intelligent Database Systems Lab