On the Relationship Between Dimensionality Reduction and Spectral Clustering from a Kernel Viewpoint. Peluffo-Ordóñez, D., H., Becerra, M., A., Castro-Ospina, A., E., Blanco-Valencia, X., Alvarado-Pérez, J., C., Therón, R., & Anaya-Isaza, A. Advances in Intelligent Systems and Computing, pages 255-264. 2016. ![Advances in Intelligent Systems and Computing [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 1 download This paper presents the development of a unified view of spectral clustering and unsupervised dimensionality reduction approaches within a generalized kernel framework. To do so, the authors propose a multipurpose latent variable model in terms of a high-dimensional representation of the input data matrix, which is incorporated into a least-squares support vector machine to yield a generalized optimization problem. After solving it via a primal-dual procedure, the final model results in a versatile projected matrix able to represent data in a low-dimensional space, as well as to provide information about clusters. Specifically, our formulation yields solutions for kernel spectral clustering and weighted-kernel principal component analysis.
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