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

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

Methodology – m-SNE

Methodology – Accelerated First-Order Method for Combination Coefficient

• Lipschitz continuous

• First order function

• Second order function

Experiments - Toy Data set

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a=

Experiments - Image Retrieval

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Experiments - Image Retrieval (cont.)

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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