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pro vyhledávání: '"Alaíz, Carlos M."'
The implementation of strategies for fault detection and diagnosis on rotating electrical machines is crucial for the reliability and safety of modern industrial systems. The contribution of this work is a methodology that combines conventional strat
Externí odkaz:
http://arxiv.org/abs/2306.09365
Publikováno v:
In Neurocomputing 7 April 2024 577
Akademický článek
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Autor:
Alaíz, Carlos M., Suykens, Johan A. K.
This work proposes a new algorithm for training a re-weighted L2 Support Vector Machine (SVM), inspired on the re-weighted Lasso algorithm of Cand\`es et al. and on the equivalence between Lasso and SVM shown recently by Jaggi. In particular, the mar
Externí odkaz:
http://arxiv.org/abs/1706.05928
A graph-based classification method is proposed for semi-supervised learning in the case of Euclidean data and for classification in the case of graph data. Our manifold learning technique is based on a convex optimization problem involving a convex
Externí odkaz:
http://arxiv.org/abs/1612.07141
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems, 99, 1-7 (2017)
In this paper, Kernel PCA is reinterpreted as the solution to a convex optimization problem. Actually, there is a constrained convex problem for each principal component, so that the constraints guarantee that the principal component is indeed a solu
Externí odkaz:
http://arxiv.org/abs/1610.06811
Publikováno v:
In Neurocomputing 7 October 2021 456:599-608
Publikováno v:
Applied and Computational Harmonic Analysis, 44, 1, 189-199 (2018)
We propose a framework for the visualization of directed networks relying on the eigenfunctions of the magnetic Laplacian, called here Magnetic Eigenmaps. The magnetic Laplacian is a complex deformation of the well-known combinatorial Laplacian. Feat
Externí odkaz:
http://arxiv.org/abs/1606.08266
Publikováno v:
Phys. Rev. E 95, 022302 (2017)
Communities in directed networks have often been characterized as regions with a high density of links, or as sets of nodes with certain patterns of connection. Our approach for community detection combines the optimization of a quality function and
Externí odkaz:
http://arxiv.org/abs/1606.07359
Publikováno v:
In Pattern Recognition March 2021 111