Braverman’s Spectrum and Matrix Diagonalization Versus iK-Means: A Unified Framework for Clustering

DOI10.1007/978-3-319-99492-5_2zbMath1518.68315OpenAlexW2888216893MaRDI QIDQ6104511

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Publication date: 28 June 2023

Published in: Braverman Readings in Machine Learning. Key Ideas from Inception to Current State (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-99492-5_2

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10) History of statistics (62-03) History of computer science (68-03)

Cites Work

• Clustering by Passing Messages Between Data Points
• Intelligent choice of the number of clusters in \(K\)-means clustering: an experimental study with different cluster spreads
• Core concepts in data analysis. Summarization, correlation and visualization.
• A sequential fitting procedure for linear data analysis models
• The method of principal clusters
• A survey of kernel and spectral methods for clustering
• Kernel Methods and Machine Learning
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