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Most informative dimension reduction
Amir Globerson
*
, Naftali Tishby
*
Corresponding author for this work
Hebrew University of Jerusalem
Research output
:
Contribution to conference
›
Paper
›
peer-review
2
Scopus citations
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Keyphrases
Dimensionality Reduction
100%
Low-dimensional Features
100%
Continuous Features
50%
Well-defined
50%
Clustering Model
50%
Bioinformatics
50%
Information Theory
50%
Mutual Information
50%
Mixture Model
50%
Feature Vector
50%
Occurrence Data
50%
Low-dimensional Representation
50%
Neural Coding
50%
Principled Approach
50%
Feature Function
50%
Approximate Sufficient Statistic
50%
Low Dimension
50%
Co-occurrence Matrix
50%
Machine Learning Datasets
50%
Learning Data Analysis
50%
Iterative Projection Algorithms
50%
Document Categorization
50%
Complex Data Analysis
50%
Representation Vector
50%
Mathematics
Wide Range
100%
Approximates
100%
Clustering
100%
Mutual Information
100%
Feature Vector
100%
Mixture Model
100%
Sufficient Statistic
100%
Occurrence Matrix
100%
Computer Science
Machine Learning
100%
Mutual Information
100%
Fundamental Problem
100%
Feature Vector
100%
Feature Function
100%
Occurrence Matrix
100%
Dimensional Feature
100%
Engineering
Joints (Structural Components)
100%
Mutual Information
100%
Feature Vector
100%
Fundamental Problem
100%
Cooccurrence Matrix
100%
Representation Vector
100%
Chemical Engineering
Learning System
100%