Rectified Gaussian kernel multi-view k-means clustering
<|reference_start|>Rectified Gaussian kernel multi-view k-means clustering: 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.<|reference_end|>
@article{sinaga2024rectified,
    title={Rectified Gaussian kernel multi-view k-means clustering},
    author={Kristina P. Sinaga},
    journal={arXiv preprint arXiv:2405.05619},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05619},
    primaryClass={cs.LG cs.CV}
}