adaptive local dissimilarity measures for discriminative dimension of labeled data
DESCRIPTION
Adaptive local dissimilarity measures for discriminative dimension of labeled data. Presenter : Kung, Chien-Hao Authors : Kerstin Bunte , Barbara Hammer, Axel Wismuller , Michael Biehl 2010,NC. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. - PowerPoint PPT PresentationTRANSCRIPT
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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
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Intelligent Database Systems Lab
OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments
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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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Intelligent Database Systems Lab
Methodology
LiRaM LVQ
Stochastic neighbor embedding
(SNE)
Exploration observation machine
(XOM)
Maximum variance unfolding
(MVU)
Charting
Locally linear embedding
(LLE)
Isomap
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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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Intelligent Database Systems Lab
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.
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Comments• Advantages– This paper is easy to read.
• Applications– Dimension reduction