Rectified Gaussian kernel multi-view k-means clustering

TL;DR

This paper introduces two novel multi-view k-means clustering algorithms that leverage Gaussian kernels and exponent distance to improve robustness and efficiency in multi-view data analysis.

Contribution

The paper proposes Gaussian-kernel multi-view k-means algorithms with a new distance measure and parameter alignment, enhancing clustering robustness over existing methods.

Findings

Demonstrated robustness on five real-world datasets

Achieved improved clustering efficiency

Validated effectiveness through numerical experiments

Abstract

In this paper, we show two new variants of multi-view k-means (MVKM) algorithms to address multi-view data. The general idea is to outline the distance between $h$-th view data points $x_i^h$ and $h$-th view cluster centers $a_k^h$ in a different manner of centroid-based approach. Unlike other methods, our proposed methods learn the multi-view data by calculating the similarity using Euclidean norm in the space of Gaussian-kernel, namely as multi-view k-means with exponent distance (MVKM-ED). By simultaneously aligning the stabilizer parameter $p$ and kernel coefficients $\beta^h$, the compression of Gaussian-kernel based weighted distance in Euclidean norm reduce the sensitivity of MVKM-ED. To this end, this paper designated as Gaussian-kernel multi-view k-means (GKMVKM) clustering algorithm. Numerical evaluation of five real-world multi-view data demonstrates the robustness and efficiency of our proposed MVKM-ED and GKMVKM approaches.