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Dimensionality-reduced subspace clustering
Reinhard Heckel
, Michael Tschannen
, Helmut Bölcskei
ETH Zurich
Publikation
:
Beitrag in Fachzeitschrift
›
Artikel
›
Begutachtung
20
Zitate (Scopus)
Übersicht
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Keyphrases
Subspace Clustering
100%
Sparse Subspace Clustering
100%
Reducing Subspace
100%
Subspace Clustering Algorithm
75%
Dimensionality Reduction
50%
Storage Requirement
25%
Performance Degradation
25%
Computational Cost
25%
Complex Constraints
25%
Undersampling
25%
Low-dimensional Space
25%
Analytical Results
25%
Orthogonal Matching Pursuit
25%
High-dimensional Data
25%
Acquisition Device
25%
Clustering Problem
25%
Speed Limit
25%
Sparse Signal Recovery
25%
Random Projection
25%
Linear Subspace
25%
High Dimensional Data Points
25%
Subspace Dimension
25%
Mathematics
Clustering
100%
Orthogonal Matching Pursuit
100%
Clustering Algorithm
100%
Thresholding
66%
Dimensionality Reduction
66%
Dimensional Data
66%
Data Point
66%
Computational Cost
33%
Synthetic Data
33%
Linear Subspace
33%
Real Data
33%
Dimensional Space
33%
Computer Science
Clustering Algorithm
100%
Orthogonal Matching Pursuit
100%
Dimensionality Reduction
66%
High Dimensional Data
66%
Lower Dimensional Space
33%
Dimensional Data Set
33%
Random Projection
33%
Dimensional Linear Subspace
33%
Computational Cost
33%
Performance Degradation
33%
Storage Requirement
33%
Engineering
Dimensionality
100%
Clustering Algorithm
60%
Orthogonal Matching Pursuit
60%
Data Point
40%
Dimensional Data
40%
Dimensional Space
20%
Performance Degradation
20%
Real Data
20%
Sparse Signal
20%
Analytical Result
20%
Storage Requirement
20%
Clustering Problem
20%
Computational Cost
20%