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Dimensionality reduction: Theoretical perspective on practical measures
Yair Bartal
*
, Nova Fandina
, Ofer Neiman
*
Corresponding author for this work
The Rachel and Selim Benin School of Engineering and Computer Science
Research output
:
Contribution to journal
›
Conference article
›
peer-review
13
Scopus citations
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Keyphrases
Dimensionality Reduction
100%
Machine Learning
60%
Average-case
40%
Machine Learning Algorithms
20%
Tight Bounds
20%
Rigorous Analysis
20%
Metric Embedding
20%
Approximation Algorithms
20%
Practical Setting
20%
Real-world Application
20%
Worst-case Behavior
20%
Distortion Measure
20%
Absolute Value
20%
Multidimensional Scaling
20%
Theoretical Research
20%
Low-dimensional Space
20%
General Metric
20%
Measurement Criteria
20%
Practice-based View
20%
Distortion Measurement
20%
Practical Applicability
20%
Computer Science
Dimensionality Reduction
100%
Machine Learning
60%
Learning System
60%
Theoretical Study
40%
Approximation Algorithms
20%
World Application
20%
Machine Learning Algorithm
20%
Oriented Analysis
20%
Distortion Measure
20%
Desired Property
20%
Theoretical Framework
20%
Multidimensional Scaling
20%
Lower Dimensional Space
20%
Mathematics
Dimensionality Reduction
100%
Worst Case
20%
Dimensional Space
20%
Central Role
20%
Absolute Value
20%
Physics
Machine Learning
100%