Rectified Gaussian kernel multi-view k-means clustering
Kristina P. Sinaga
TL;DR
This work addresses multi-view clustering by rectifying distance measures used in MVKM. It introduces two Gaussian-kernel based variants, MVKM-ED and GKMVKM, which jointly learn view weights and kernel parameters to improve robustness to noise and view heterogeneity. Key contributions include formulating exponent-distance based and power-penalized kernel distances, deriving update rules for memberships, centers, and view weights, and proposing practical strategies to set the stabilizer $p$ and kernel coefficients $\beta^h$. Extensive experiments on five real-world datasets plus synthetic data demonstrate superior performance and scalability compared to existing multi-view clustering methods. The proposed framework offers a robust, kernel-based approach for multi-view clustering with clear applicability to high-dimensional and large-scale data.
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 $β^h$, 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.