On strong consistency of kernel \(k\)-means: a Rademacher complexity approach

DOI10.1016/j.spl.2021.109291zbMath1478.62158OpenAlexW3209071142MaRDI QIDQ2070586

Swagatam Das, Anish Chakrabarty

Publication date: 24 January 2022

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2021.109291

zbMATH Keywords

strong consistencyRademacher complexitykernel \(k\)-means clustering

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• Unnamed Item
• Fast rates for empirical vector quantization
• Strong consistency of k-means clustering
• Consistency of spectral clustering
• A Vector-Contraction Inequality for Rademacher Complexities
• Individual Convergence Rates in Empirical Vector Quantizer Design
• On the Performance of Clustering in Hilbert Spaces
• High-Dimensional Statistics
• The minimax distortion redundancy in empirical quantizer design
• Least squares quantization in PCM
• 10.1162/153244303321897690
• Rates of convergence in the source coding theorem, in empirical quantizer design, and in universal lossy source coding

This page was built for publication: On strong consistency of kernel \(k\)-means: a Rademacher complexity approach