Intelligent Database Systems Lab

Presenter : Kung, Chien-Hao

Authors : Kerstin Bunte, Barbara Hammer, Axel Wismuller,

Michael Biehl

2010,NC

Adaptive local dissimilarity measures for discriminative dimension of labeled data

Intelligent Database Systems Lab

OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments

Intelligent Database Systems Lab

Motivation• Dimension reduction embedding in lower

dimensions necessarily includes a loss of

information.

• To explicitly control the information kept by a

specific dimension reduction technique are

highly desirable.

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Objectives• The aim of this paper is to combine an adaptive metric

and recent visualization techniques towards a

discriminative approach.

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Methodology

LiRaM LVQ

Stochastic neighbor embedding

(SNE)

Exploration observation machine

(XOM)

Maximum variance unfolding

(MVU)

Charting

Locally linear embedding

(LLE)

Isomap

Intelligent Database Systems Lab

LiRaM LVQ– Prototype based classifier, extension of LVQ– Modified Euclidean distance:

– Adapt local matrices during training(minimize a cost function by gradient descent)

Methodology

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Combination of local linear patches by charting• The charting technique can decompose the sample data

into locally linear patches and combine them into a single low-dimensional coordinate system.

Methodology

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Locally linear embedding (LLE)• Locally linear embedding (LLE) uses the criterion of

topology preservation for dimension reduction.

Methodology

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Isomap• Isomap is an extension of metric Multi-Dimensional

Scaling(MDS) which uses distance preservation as criterion

Methodology

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Stochastic neighbor embedding (SNE)• Stochastic neighbor embedding (SNE) constitutes an

unsupervised projection which follows a probability based approach.

Methodology

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Exploration observation machine(XOM)• The exploratory observation machine (XOM) has

recently been introduced as a novel computational framework for structure-preserving dimension reduction.

Methodology

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Maximum variance unfolding(MVU)• Maximum variance unfolding(MVU) is a dimension

reduction technique which aims at preservation if local distances.

Methodology

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Experiments

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ExperimentsThree tip star data set

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ExperimentsWine data set

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ExperimentsSegmentationdata set

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ExperimentsUSPSdata set

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Experiments

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Conclusions• The results are quite diverse and no single method

which is optimum for every case an be identified.

• In general, discriminative visualization as introduced

in this paper improves all the corresponding

unsupervised methods.

Intelligent Database Systems Lab

Comments• Advantages– This paper is easy to read.

• Applications– Dimension reduction