# Kernel Correlation-Dissimilarity for Multiple Kernel k-Means Clustering

arXiv: https://arxiv.org/abs/2403.03448 · HTML: https://mlsanity.com/paper/2403.03448/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Deep Spectral Clustering using Dual Autoencoder Network](https://mlsanity.com/paper/1904.13113/) (2019)
- [A Survey on Multi-View Clustering](https://mlsanity.com/paper/1712.06246/) (2017)
- [SimpleMKKM: Simple Multiple Kernel K-means](https://mlsanity.com/paper/2005.04975/) (2020)
- [Multiple Kernel k-Means Clustering by Selecting Representative Kernels](https://mlsanity.com/paper/1811.00264/) (2018)
- [How to Use K-means for Big Data Clustering?](https://mlsanity.com/paper/2204.07485/) (2022)

## Built on by (most cited first, 2 of 2)

- [Towards Robust Nonlinear Subspace Clustering: A Kernel Learning Approach](https://mlsanity.com/paper/2501.06368/) (2025)
- [Wasserstein-based Kernel Principal Component Analysis for Clustering Applications](https://mlsanity.com/paper/2503.14357/) (2025)

## Abstract (arXiv)

The main objective of the Multiple Kernel k-Means (MKKM) algorithm is to extract non-linear information and achieve optimal clustering by optimizing base kernel matrices. Current methods enhance information diversity and reduce redundancy by exploiting interdependencies among multiple kernels based on correlations or dissimilarities. Nevertheless, relying solely on a single metric, such as correlation or dissimilarity, to define kernel relationships introduces bias and incomplete characterization. Consequently, this limitation hinders efficient information extraction, ultimately compromising clustering performance. To tackle this challenge, we introduce a novel method that systematically integrates both kernel correlation and dissimilarity. Our approach comprehensively captures kernel relationships, facilitating more efficient classification information extraction and improving clustering performance. By emphasizing the coherence between kernel correlation and dissimilarity, our method offers a more objective and transparent strategy for extracting non-linear information and significantly improving clustering precision, supported by theoretical rationale. We assess the performance of our algorithm on 13 challenging benchmark datasets, demonstrating its superiority over contemporary state-of-the-art MKKM techniques.