# Kernel Spectral Clustering

Source: [Kernel Spectral Clustering](<https://www.sota2.com/research/paper/68fa52ec1e209131ce97f088>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T12:10:02.267Z

Authors: Ilaria Giulini

Published: 2016-06-21T11:17:45.000Z

## Abstract

We investigate the question of studying spectral clustering in a Hilbert space where the set of points to cluster are drawn i.i.d. according to an unknown probability distribution whose support is a union of compact connected components. We modify the algorithm proposed by Ng, Jordan and Weiss in order to propose a new algorithm that automatically estimates the number of clusters and we characterize the convergence of this new algorithm in terms of convergence of Gram operators. We also give a hint of how this approach may lead to learn transformation-invariant representations in the context of image classification.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1606.06519>)

[PDF](<https://arxiv.org/pdf/1606.06519.pdf>)