Multiple Kernel Clustering via Local Regression Integration

TL;DR

This paper introduces a robust multiple kernel clustering method that captures local data structures and reduces noise sensitivity by integrating local regression coefficients, outperforming existing methods on benchmark datasets.

Contribution

It proposes a novel multiple kernel clustering approach using local regression integration, improving robustness and reducing variable complexity compared to prior methods.

Findings

Outperforms 10 state-of-the-art methods on benchmark datasets

Reduces variable complexity and noise sensitivity

Effectively captures local data structures

Abstract

Multiple kernel methods less consider the intrinsic manifold structure of multiple kernel data and estimate the consensus kernel matrix with quadratic number of variables, which makes it vulnerable to the noise and outliers within multiple candidate kernels. This paper first presents the clustering method via kernelized local regression (CKLR). It captures the local structure of kernel data and employs kernel regression on the local region to predict the clustering results. Moreover, this paper further extends it to perform clustering via the multiple kernel local regression (CMKLR). We construct the kernel level local regression sparse coefficient matrix for each candidate kernel, which well characterizes the kernel level manifold structure. We then aggregate all the kernel level local regression coefficients via linear weights and generate the consensus sparse local regression coefficient, which largely reduces the number of candidate variables and becomes more robust against noises and outliers within multiple kernel data. Thus, the proposed method CMKLR avoids the above two limitations. It only contains one additional hyperparameter for tuning. Extensive experimental results show that the clustering performance of the proposed method on benchmark datasets is better than that of 10 state-of-the-art multiple kernel clustering methods.