Dimensionality reduction via subspace and submanifold learning
Published
Journal Article (Review)
The problem of finding and exploiting low-dimensional structures in high-dimensional data is taking on increasing importance in image, video, or audio processing; Web data analysis/search; and bioinformatics, where data sets now routinely lie in observational spaces of thousands, millions, or even billions of dimensions. The curse of dimensionality is in full play here: We often need to conduct meaningful inference with a limited number of samples in a very high-dimensional space. Conventional statistical and computational tools have become severely inadequate for processing and analyzing such high-dimensional data. © 2006 IEEE.
Full Text
Duke Authors
Cited Authors
- Ma, Y; Niyogi, P; Sapiro, G; Vidal, R
Published Date
- January 1, 2011
Published In
Volume / Issue
- 28 / 2
International Standard Serial Number (ISSN)
- 1053-5888
Digital Object Identifier (DOI)
- 10.1109/MSP.2010.940005
Citation Source
- Scopus