Sparse embedding: A framework for sparsity promoting dimensionality reduction

Hien V. Nguyen; Nasser M. Nasrabadi; Rama Chellappa

doi:10.1007/978-3-642-33783-3_30

Sparse embedding: A framework for sparsity promoting dimensionality reduction

Hien V. Nguyen, Nasser M. Nasrabadi, Rama Chellappa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

42 Scopus citations

Abstract

We introduce a novel framework, called sparse embedding (SE), for simultaneous dimensionality reduction and dictionary learning. We formulate an optimization problem for learning a transformation from the original signal domain to a lower-dimensional one in a way that preserves the sparse structure of data. We propose an efficient optimization algorithm and present its non-linear extension based on the kernel methods. One of the key features of our method is that it is computationally efficient as the learning is done in the lower-dimensional space and it discards the irrelevant part of the signal that derails the dictionary learning process. Various experiments show that our method is able to capture the meaningful structure of data and can perform significantly better than many competitive algorithms on signal recovery and object classification tasks.

Original language	American English
Title of host publication	Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings
Pages	414-427
Number of pages	14
Edition	PART 6
DOIs	https://doi.org/10.1007/978-3-642-33783-3_30
State	Published - 2012
Externally published	Yes
Event	12th European Conference on Computer Vision, ECCV 2012 - Florence, Italy Duration: Oct 7 2012 → Oct 13 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 6
Volume	7577 LNCS

Conference

Conference	12th European Conference on Computer Vision, ECCV 2012
Country/Territory	Italy
City	Florence
Period	10/7/12 → 10/13/12

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-33783-3_30

Cite this

Nguyen, H. V., Nasrabadi, N. M., & Chellappa, R. (2012). Sparse embedding: A framework for sparsity promoting dimensionality reduction. In Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings (PART 6 ed., pp. 414-427). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7577 LNCS, No. PART 6). https://doi.org/10.1007/978-3-642-33783-3_30

Nguyen, Hien V. ; Nasrabadi, Nasser M. ; Chellappa, Rama. / Sparse embedding : A framework for sparsity promoting dimensionality reduction. Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings. PART 6. ed. 2012. pp. 414-427 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 6).

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author = "Nguyen, {Hien V.} and Nasrabadi, {Nasser M.} and Rama Chellappa",

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Nguyen, HV, Nasrabadi, NM & Chellappa, R 2012, Sparse embedding: A framework for sparsity promoting dimensionality reduction. in Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings. PART 6 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 6, vol. 7577 LNCS, pp. 414-427, 12th European Conference on Computer Vision, ECCV 2012, Florence, Italy, 10/7/12. https://doi.org/10.1007/978-3-642-33783-3_30

Sparse embedding: A framework for sparsity promoting dimensionality reduction. / Nguyen, Hien V.; Nasrabadi, Nasser M.; Chellappa, Rama.
Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings. PART 6. ed. 2012. p. 414-427 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7577 LNCS, No. PART 6).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - We introduce a novel framework, called sparse embedding (SE), for simultaneous dimensionality reduction and dictionary learning. We formulate an optimization problem for learning a transformation from the original signal domain to a lower-dimensional one in a way that preserves the sparse structure of data. We propose an efficient optimization algorithm and present its non-linear extension based on the kernel methods. One of the key features of our method is that it is computationally efficient as the learning is done in the lower-dimensional space and it discards the irrelevant part of the signal that derails the dictionary learning process. Various experiments show that our method is able to capture the meaningful structure of data and can perform significantly better than many competitive algorithms on signal recovery and object classification tasks.

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Nguyen HV, Nasrabadi NM, Chellappa R. Sparse embedding: A framework for sparsity promoting dimensionality reduction. In Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings. PART 6 ed. 2012. p. 414-427. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 6). doi: 10.1007/978-3-642-33783-3_30

Sparse embedding: A framework for sparsity promoting dimensionality reduction

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