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Spectral Subspace Clustering: Algorithms and Practical Extensions
Stephen Tierney
Research output
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Thesis
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Doctoral Thesis
200
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Dive into the research topics of 'Spectral Subspace Clustering: Algorithms and Practical Extensions'. Together they form a unique fingerprint.
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Keyphrases
Subspace Clustering
100%
Subspace Structure
100%
Sparse Subspace Clustering
100%
Subspace Clustering Algorithm
100%
Clustering Accuracy
100%
Spectral Subspaces
100%
Clustering Algorithm
50%
Computational Requirements
50%
Imperfection
50%
Point-based
50%
Low-rank Representation
50%
Clustered Data
50%
Memory Requirements
50%
Robust Clustering
50%
Non-Euclidean Data
50%
Multiple Observations
50%
Open-source Library
50%
Non-linear Subspace
50%
Challenging Problems
50%
Computer Science
Clustering Algorithm
100%
Low-Rank Representation
50%
Single Application
50%
Segment (Data)
50%
Open-Source Library
50%
Clustered Data
50%
Multiple Observation
50%
Memory Requirement
50%
Linear Subspace
50%