Download - Presenter : Wei- Hao Huang Author : Bo Xie , Yang Mu, Dacheng Tao , Kaiqi Huang TSMCA , 2011
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Presenter: Wei-Hao Huang
Author: Bo Xie, Yang Mu, Dacheng Tao, Kaiqi Huang
TSMCA, 2011
m-SNE: Multiview Stochastic Neighbor Embedding
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Outlines
MotivationObjectivesMethodologyExperimentsConclusionsComments
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Motivation• To duly utilize different features or multiview data is a
challenge
Conventional strategies
Different statistical properties are not considered
Different features are not well explored
Corrupting by noise
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Objectives
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• To propose a multiview stochastic neighbor embedding to unify different features under a probabilistic framework.
m-SNE
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Methodology - Framework
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Methodology – m-SNE
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Methodology – Accelerated First-Order Method for Combination Coefficient
• Lipschitz continuous
• First order function
• Second order function
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Experiments - Toy Data set
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Experiments - Image Retrieval
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Experiments - Image Retrieval (cont.)
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Experiments - Object Categorization
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Experiments - Scene Recognition
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Conclusions• m-SNE is able to meaningfully integrates different
views.• The combination coefficient can – exploit complementary information in different
view– suppress noise
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Comments• Advantages– m-SNE can integrate different views
• Applications– Dimension reduction, image retrieval and
multiview learning
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