Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
Learning Exemplar-Represented Manifolds in Latent Space for Classification
verfasst von : Shu Kong, Donghui Wang
Erschienen in: Machine Learning and Knowledge Discovery in Databases
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Intrinsic manifold structure of a data collection is valuable information for classification task. By considering the manifold structure in the data set for classification and with the sparse coding framework, we propose an algorithm to: (1) find exemplars from each class to represent the class-specific manifold structure, in which way the object-space dimensionality is reduced; (2) simultaneously learn a latent feature space to make the mapped data more discriminative according to the class-specific manifold measurement. We call the proposed algorithm Exemplar-represented Manifold in Latent Space for Classification (EMLSC). We also present the nonlinear extension of EMLSC based on kernel tricks to deal with highly nonlinear situations. Experiments on synthetic and real-world datasets demonstrate the merit of the proposed method.