Implicit Annealing in Kernel Spaces: A Strongly Consistent Clustering Approach-Reference-Cited by-同舟云学术

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Implicit Annealing in Kernel Spaces: A Strongly Consistent Clustering Approach

Published:2022 Issue: Volume: Page:1-10
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Paul Debolina¹,Chakraborty Saptarshi²^ORCID,Das Swagatam³^ORCID,Xu Jason⁴

Affiliation:

1. Department of Statistics, Stanford University, USA

2. Department of Statistics, University of California, Berkeley, USA

3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

4. Department of Statistical Science, Duke University, Engalnd

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/4359286/09928792.pdf?arnumber=9928792

Reference73 articles.

1. Scalable kernel k-means clustering with Nyström approximation: Relative-error bounds;wang;J Mach Learn Res,2019

2. Local Rademacher complexities

3. Kernel K-Means Sampling for Nyström Approximation

4. Rademacher and gaussian complexities: Risk bounds and structural results;bartlett;J Mach Learn Res,2002

5. Unified Spectral Clustering With Optimal Graph

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1. On the Consistency and Large-Scale Extension of Multiple Kernel Clustering;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-10

2. Fast Fusion Clustering via Double Random Projection;Entropy;2024-04-28

3. Sparse kernel k-means for high-dimensional data;Pattern Recognition;2023-12

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