presenter : wei- hao huang authors : miguel ´ a. carreira-perpi˜ n´an icml , 2010
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The Elastic Embedding Algorithm for Dimensionality Reduction. Presenter : Wei- Hao Huang Authors : Miguel ´ A. Carreira-Perpi˜ n´an ICML , 2010. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation. - PowerPoint PPT PresentationTRANSCRIPT
Intelligent Database Systems Lab
Presenter : Wei-Hao Huang
Authors : Miguel ´ A. Carreira-Perpi˜n´an
ICML, 2010
The Elastic Embedding Algorithm for Dimensionality Reduction
Intelligent Database Systems Lab
OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments
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Motivation• The disadvantage of dimensionality reduction
– Difficult to understand their objective function.
– Optimisation is costly and prone to local optima.
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Objectives• To propose a new dimensionality reduction
More efficient and robust
Further our understanding algorithms
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Methodology - Framework
Elastic Embedding High dimension dataset
Low dimension data
Laplacian eigenmaps SNE+
Objective function
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Methodology – Elastic Embedding
• Object function
• Gradient of E
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Methodology - Study of λ
• N=2
• N>2
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Methodology – Out of sample
• Objective function
• Mapping and reconstruction mappings
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Experiments – 2D spiral
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Experiments – Swiss roll
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Experiments – COIL-20 dataset
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Conclusions• EE dimensionality reduction improves over SNE
methods.
• EE produces better quality more quickly and robustly.
• All of ideas can be directly applied to SNE, t-SNE and
earlier algorithms.
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Comments• Advantages– EE improves disadvantage of SNE on different
versions• Applications– Dimensionality Reduction