dimensionality reduction mappings

Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

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Dimensionality Reduction Mappings

Presenter : Wei-Hao Huang 　Authors : Kerstin Bunte, Michael Biehl, Barbara Hammer

CIDM, 2011


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Outlines Motivation Objectives Methodology Experiments Conclusions Comments


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Motivation· Providing a mapping of a priorly given finite set

of points only, requiring additional steps for out-of-sample extensions.

Dimensionality Reduction(tSNE, MDS, Isomap)Old data

New data

Map


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I. M.Objectives

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• To propose general view on dimensionality reduction based on the concept of cost functions, and based on this general principle

Dimensionality ReductionPrior (tSNE, MDS, Isomap)Old data

New data

Map


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I. M.Methodology· General View

· General Principle

· Generalization Ability

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Methodology

Data

Characteristics of data(Euclidean distance)

Characteristics of projections(Euclidean distance)

Error measure(Cost function)


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I. M.Methodology· General Principle

· Apply on tSNE─ Global linear mapping

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I. M.Methodology· Apply on tSNE

─ Local linear mappings

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I. M.Methodology· Generalization Ability

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I. M.Experiments· Unsupervised clustering

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I. M.Conclusions· The paper opens a way towards a theory of

data visualization taking the perspective of its generalization ability to new data points.

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Comments· Advantages

─ This paper opens a way towards a theory of data visualization

· Applications─ Dimensionality reduction

dimensionality reduction mappings

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