Kernel Alignment for Unsupervised Feature Selection via Matrix Factorization
<|reference_start|>Kernel Alignment for Unsupervised Feature Selection via Matrix Factorization: By removing irrelevant and redundant features, feature selection aims to find a good representation of the original features. With the prevalence of unlabeled data, unsupervised feature selection has been proven effective in alleviating the so-called curse of dimensionality. Most existing matrix factorization-based unsupervised feature selection methods are built upon subspace learning, but they have limitations in capturing nonlinear structural information among features. It is well-known that kernel techniques can capture nonlinear structural information. In this paper, we construct a model by integrating kernel functions and kernel alignment, which can be equivalently characterized as a matrix factorization problem. However, such an extension raises another issue: the algorithm performance heavily depends on the choice of kernel, which is often unknown a priori. Therefore, we further propose a multiple kernel-based learning method. By doing so, our model can learn both linear and nonlinear similarity information and automatically generate the most appropriate kernel. Experimental analysis on real-world data demonstrates that the two proposed methods outperform other classic and state-of-the-art unsupervised feature selection methods in terms of clustering results and redundancy reduction in almost all datasets tested.<|reference_end|>
@article{lin2024kernel,
    title={Kernel Alignment for Unsupervised Feature Selection via Matrix
  Factorization},
    author={Ziyuan Lin and Deanna Needell},
    journal={arXiv preprint arXiv:2403.14688},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14688},
    primaryClass={cs.LG cs.NA math.NA}
}