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.
Intelligent Database Systems Lab
Objectives• The aim of this paper is to combine an adaptive metric
and recent visualization techniques towards a
discriminative approach.
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
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
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
Intelligent Database Systems Lab
Locally linear embedding (LLE)• Locally linear embedding (LLE) uses the criterion of
topology preservation for dimension reduction.
Methodology
Intelligent Database Systems Lab
Isomap• Isomap is an extension of metric Multi-Dimensional
Scaling(MDS) which uses distance preservation as criterion
Methodology
Intelligent Database Systems Lab
Stochastic neighbor embedding (SNE)• Stochastic neighbor embedding (SNE) constitutes an
unsupervised projection which follows a probability based approach.
Methodology
Intelligent Database Systems Lab
Exploration observation machine(XOM)• The exploratory observation machine (XOM) has
recently been introduced as a novel computational framework for structure-preserving dimension reduction.
Methodology
Intelligent Database Systems Lab
Maximum variance unfolding(MVU)• Maximum variance unfolding(MVU) is a dimension
reduction technique which aims at preservation if local distances.
Methodology
Intelligent Database Systems Lab
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
Intelligent Database Systems Lab
Experiments
Intelligent Database Systems Lab
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
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