Rectified Gaussian kernel multi-view k-means clustering
This work addresses multi-view clustering, a domain-specific problem in machine learning, with incremental improvements over existing methods.
The paper tackles multi-view data clustering by proposing two new variants of multi-view k-means algorithms, MVKM-ED and GKMVKM, which use Gaussian-kernel similarity and Euclidean norm to reduce sensitivity, with numerical evaluations on five real-world datasets demonstrating robustness and efficiency.
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.