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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. Presenter : Wei- Hao Huang Author : Bo Xie , Yang Mu, Dacheng Tao , Kaiqi Huang TSMCA , 2011. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Presenter : Wei- Hao Huang Author : Bo Xie , Yang Mu, Dacheng Tao , Kaiqi Huang TSMCA , 2011

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  1. m-SNE: Multiview Stochastic Neighbor Embedding Presenter: Wei-Hao HuangAuthor:Bo Xie, Yang Mu, DachengTao, Kaiqi HuangTSMCA, 2011

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • To duly utilize different features or multiview data is a challenge Different statistical properties are not considered Different features are not well explored Conventional strategies Corrupting by noise

  4. Objectives • To propose a multiview stochastic neighbor embedding to unify different features under a probabilistic framework. m-SNE

  5. Methodology - Framework

  6. Methodology – m-SNE

  7. Methodology – Accelerated First-Order Method for Combination Coefficient • Lipschitz continuous • First order function • Second order function

  8. Experiments - Toy Data set a=

  9. Experiments - Image Retrieval

  10. Experiments - Image Retrieval (cont.)

  11. Experiments - Object Categorization

  12. Experiments - Scene Recognition

  13. Conclusions • m-SNE is able to meaningfully integrates different views. • The combination coefficient can • exploit complementary information in different view • suppress noise

  14. Comments • Advantages • m-SNE can integrate different views • Applications • Dimension reduction, image retrieval and multiview learning

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