Anzeige
Erschienen in:
2008 | OriginalPaper | Buchkapitel
Distortion-Free Nonlinear Dimensionality Reduction
verfasst von : Yangqing Jia, Zheng Wang, Changshui Zhang
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
Nonlinear Dimensionality Reduction is an important issue in many machine learning areas where essentially low-dimensional data is nonlinearly embedded in some high-dimensional space. In this paper, we show that the existing Laplacian Eigenmaps method suffers from the distortion problem, and propose a new distortion-free dimensionality reduction method by adopting a local linear model to encode the local information. We introduce a new loss function that can be seen as a different way to construct the Laplacian matrix, and a new way to impose scaling constraints under the local linear model. Better low-dimensional embeddings are obtained via constrained concave convex procedure. Empirical studies and real-world applications have shown the effectiveness of our method.