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Geometric component analysis and its applications to data analysis
Amit Bermanis
, Moshe Salhov
,
Amir Averbuch
*
*
Corresponding author for this work
School of Computer Science and AI
University of Toronto
Research output
:
Contribution to journal
›
Article
›
peer-review
1
Scopus citations
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Keyphrases
Dimensionality Reduction
100%
Geometric Components
100%
Diffusion Maps
50%
Anomaly Detection
50%
Nonlinear Data
50%
Learning Task
25%
Principal Coordinate Analysis (PCoA)
25%
Dictionary
25%
Greedy Algorithm
25%
Ambient Space
25%
Dictionary-based
25%
Linear Subspace
25%
Diffusion Kernel
25%
Unsupervised Learning
25%
PCA-based
25%
Classification Detection
25%
Machine Learning Tasks
25%
Low Computational Complexity
25%
Greedy Approach
25%
Curse of Dimensionality
25%
Diffusion Geometry
25%
Out-of-sample Extension
25%
Kernel Matrix
25%
Nonlinear Diffusion
25%
Distortion Rate
25%
Main Algorithm
25%
Multidimensional Data
25%
Geometric Base
25%
Computational Tractability
25%
High-dimensional Big Data
25%
Landmark Data
25%
Computer Science
Component Analysis
100%
Dimensionality Reduction
100%
Geometric Component
100%
Anomaly Detection
50%
Big Data
25%
Computational Complexity
25%
Unsupervised Learning
25%
Principal Components
25%
Analysis Methodology
25%
Multidimensional Data
25%
Linear Subspace
25%
Remaining Data
25%
Landmark Data
25%
Distortion Rate
25%
Machine Learning
25%
Learning System
25%
Greedy Algorithm
25%
Engineering
Data Point
100%
Dimensionality
100%
Component Analysis
100%
Anomaly Detection
40%
Learning Task
40%
Direct Application
20%
Computational Complexity
20%
Good Result
20%
Principal Components
20%
Greedy Approach
20%
Distortion Rate
20%
Linear Form
20%
Learning System
20%
Greedy Algorithm
20%
Big Data
20%
Mathematics
Data Point
100%
Dimensionality Reduction
80%
Nonlinear
60%
Learning Task
40%
Principal Component Analysis
40%
Reduction Method
20%
Matrix (Mathematics)
20%
Greedy Algorithm
20%
Ambient Space
20%
Real-World Data
20%
Linear Subspace
20%
Curse of Dimensionality
20%
Remaining Data
20%