K-means algorithms for functional data

López García, María Luz; García-Rodenas, Ricardo y González Gómez, Antonia (2015). K-means algorithms for functional data. "NEUROCOMPUTING", v. 151 ; pp. 231-245. ISSN 0925-2312.

Descripción

Título: K-means algorithms for functional data
Autor/es:
  • López García, María Luz
  • García-Rodenas, Ricardo
  • González Gómez, Antonia
Tipo de Documento: Artículo
Título de Revista/Publicación: NEUROCOMPUTING
Fecha: 2015
Volumen: 151
Materias:
Palabras Clave Informales: Functional data, K-means, Reproducing Kernel Hilbert Space.
Escuela: E.T.S.I. Montes (UPM) [antigua denominación]
Departamento: Matemática Aplicada
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Cluster analysis of functional data considers that the objects on which you want to perform a taxonomy are functions f : X e Rp ↦R and the available information about each object is a sample in a finite set of points f ¼ fðx ; y ÞA X x Rgn . The aim is to infer the meaningful groups by working explicitly with its infinite-dimensional nature. In this paper the use of K-means algorithms to solve this problem is analysed. A comparative study of three K-means algorithms has been conducted. The K-means algorithm for raw data, a kernel K-means algorithm for raw data and a K-means algorithm using two distances for functional data are tested. These distances, called dVn and dϕ, are based on projections onto Reproducing Kernel Hilbert Spaces (RKHS) and Tikhonov regularization theory. Although it is shown that both distances are equivalent, they lead to two different strategies to reduce the dimensionality of the data. In the case of dVn distance the most suitable strategy is Johnson–Lindenstrauss random projections. The dimensionality reduction for dϕ is based on spectral methods.

Más información

ID de Registro: 44822
Identificador DC: http://oa.upm.es/44822/
Identificador OAI: oai:oa.upm.es:44822
Depositado por: Memoria Investigacion
Depositado el: 14 Mar 2017 12:17
Ultima Modificación: 19 Abr 2017 08:53
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