Dimensionality reduction via subspace and submanifold learning
Publication
, Journal Article
Ma, Y; Niyogi, P; Sapiro, G; Vidal, R
Published in: IEEE Signal Processing Magazine
January 1, 2011
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.
Duke Scholars
Published In
IEEE Signal Processing Magazine
DOI
ISSN
1053-5888
Publication Date
January 1, 2011
Volume
28
Issue
2
Related Subject Headings
- Networking & Telecommunications
- 4603 Computer vision and multimedia computation
- 4006 Communications engineering
- 0913 Mechanical Engineering
- 0906 Electrical and Electronic Engineering
- 0801 Artificial Intelligence and Image Processing
Citation
APA
Chicago
ICMJE
MLA
NLM
Ma, Y., Niyogi, P., Sapiro, G., & Vidal, R. (2011). Dimensionality reduction via subspace and submanifold learning. IEEE Signal Processing Magazine, 28(2). https://doi.org/10.1109/MSP.2010.940005
Ma, Y., P. Niyogi, G. Sapiro, and R. Vidal. “Dimensionality reduction via subspace and submanifold learning.” IEEE Signal Processing Magazine 28, no. 2 (January 1, 2011). https://doi.org/10.1109/MSP.2010.940005.
Ma Y, Niyogi P, Sapiro G, Vidal R. Dimensionality reduction via subspace and submanifold learning. IEEE Signal Processing Magazine. 2011 Jan 1;28(2).
Ma, Y., et al. “Dimensionality reduction via subspace and submanifold learning.” IEEE Signal Processing Magazine, vol. 28, no. 2, Jan. 2011. Scopus, doi:10.1109/MSP.2010.940005.
Ma Y, Niyogi P, Sapiro G, Vidal R. Dimensionality reduction via subspace and submanifold learning. IEEE Signal Processing Magazine. 2011 Jan 1;28(2).
Published In
IEEE Signal Processing Magazine
DOI
ISSN
1053-5888
Publication Date
January 1, 2011
Volume
28
Issue
2
Related Subject Headings
- Networking & Telecommunications
- 4603 Computer vision and multimedia computation
- 4006 Communications engineering
- 0913 Mechanical Engineering
- 0906 Electrical and Electronic Engineering
- 0801 Artificial Intelligence and Image Processing