@inbook{
 type = {inbook},
 year = {2013},
 keywords = {Dynamic data,Kernels,Spectral clustering,Support vector machines},
 pages = {238-245},
 websites = {http://link.springer.com/10.1007/978-3-642-41822-8_30},
 id = {4f71d931-77c4-380d-a3f9-8cc20c314343},
 created = {2022-01-26T03:01:01.072Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},
 last_modified = {2022-01-26T03:01:01.072Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Peluffo-Ordonez2013c},
 private_publication = {false},
 abstract = {This paper introduces a novel spectral clustering approach based on kernels to analyze time-varying data. Our approach is developed within a multiple kernel learning framework, which, in this case is assumed as a linear combination model. To perform such linear combination, weighting factors are estimated by a ranking procedure yielding a vector calculated from the eigenvectors-derived-clustering-method. Particularly, the method named kernel spectral clustering is considered. Proposed method is compared to some conventional spectral clustering techniques, namely, kernel k-means and min-cuts. Standard k-means as well. The clustering performance is quantified by the normalized mutual information and Adjusted Rand Index measures. Experimental results prove that proposed approach is an useful tool for both tracking and clustering dynamic data, being able to manage applications for human motion analysis. © Springer-Verlag 2013.},
 bibtype = {inbook},
 author = {Peluffo-Ordóñez, Diego Hernán and García-Vega, Sergio and Álvarez-Meza, Andrés Marino and Castellanos-Domínguez, César Germán},
 doi = {10.1007/978-3-642-41822-8_30},
 chapter = {Kernel Spectral Clustering for Dynamic Data},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

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