Dimensionality reduction via subspace and submanifold learning


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