| Autor: |
Xiomara Patricia BLANCO VALENCIA, M. A. BECERRA, A. E. CASTRO OSPINA, M. ORTEGA ADARME, D. VIVEROS MELO, D. H. PELUFFO ORDÓÑEZ |
| Jazyk: |
angličtina |
| Rok vydání: |
2017 |
| Předmět: |
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| Zdroj: |
Advances in Distributed Computing and Artificial Intelligence Journal, Vol 6, Iss 1, Pp 31-40 (2017) |
| Druh dokumentu: |
article |
| ISSN: |
2255-2863 |
| DOI: |
10.14201/ADCAIJ2017613140 |
| Popis: |
This work outlines a unified formulation to represent spectral approaches for both dimensionality reduction and clustering. Proposed formulation starts with a generic latent variable model in terms of the projected input data matrix.Particularly, such a projection maps data onto a unknown high-dimensional space. Regarding this model, a generalized optimization problem is stated using quadratic formulations and a least-squares support vector machine.The solution of the optimization is addressed through a primal-dual scheme.Once latent variables and parameters are determined, the resultant model outputs a versatile projected matrix able to represent data in a low-dimensional space, as well as to provide information about clusters. Particularly, proposedformulation yields solutions for kernel spectral clustering and weighted-kernel principal component analysis. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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