Fused sparsity and robust estimation for linear models with unknown variance

Yin Chen; Arnak S. Dalalyan

Fused sparsity and robust estimation for linear models with unknown variance

Yin Chen
, Arnak S. Dalalyan

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

Abstract

In this paper, we develop a novel approach to the problem of learning sparse representations in the context of fused sparsity and unknown noise level. We propose an algorithm, termed Scaled Fused Dantzig Selector (SFDS), that accomplishes the aforementioned learning task by means of a second-order cone program. A special emphasize is put on the particular instance of fused sparsity corresponding to the learning in presence of outliers. We establish finite sample risk bounds and carry out an experimental evaluation on both synthetic and real data.

Original language	English
Title of host publication	Advances in Neural Information Processing Systems 25
Subtitle of host publication	26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012
Pages	1259-1267
Number of pages	9
Publication status	Published - 1 Dec 2012
Externally published	Yes
Event	26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 - Lake Tahoe, NV, United States Duration: 3 Dec 2012 → 6 Dec 2012

Publication series

Name	Advances in Neural Information Processing Systems
Volume	2
ISSN (Print)	1049-5258

Conference

Conference	26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012
Country/Territory	United States
City	Lake Tahoe, NV
Period	3/12/12 → 6/12/12

Cite this

@inproceedings{d6900b013ce44ea7b63b24ce4f32d669,

title = "Fused sparsity and robust estimation for linear models with unknown variance",

abstract = "In this paper, we develop a novel approach to the problem of learning sparse representations in the context of fused sparsity and unknown noise level. We propose an algorithm, termed Scaled Fused Dantzig Selector (SFDS), that accomplishes the aforementioned learning task by means of a second-order cone program. A special emphasize is put on the particular instance of fused sparsity corresponding to the learning in presence of outliers. We establish finite sample risk bounds and carry out an experimental evaluation on both synthetic and real data.",

author = "Yin Chen and Dalalyan, \{Arnak S.\}",

year = "2012",

month = dec,

day = "1",

language = "English",

isbn = "9781627480031",

series = "Advances in Neural Information Processing Systems",

pages = "1259--1267",

booktitle = "Advances in Neural Information Processing Systems 25",

note = "26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 ; Conference date: 03-12-2012 Through 06-12-2012",

}

Chen, Y & Dalalyan, AS 2012, Fused sparsity and robust estimation for linear models with unknown variance. in Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012. Advances in Neural Information Processing Systems, vol. 2, pp. 1259-1267, 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012, Lake Tahoe, NV, United States, 3/12/12.

Fused sparsity and robust estimation for linear models with unknown variance. / Chen, Yin; Dalalyan, Arnak S.
Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012. 2012. p. 1259-1267 (Advances in Neural Information Processing Systems; Vol. 2).

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

TY - GEN

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AU - Dalalyan, Arnak S.

PY - 2012/12/1

Y1 - 2012/12/1

N2 - In this paper, we develop a novel approach to the problem of learning sparse representations in the context of fused sparsity and unknown noise level. We propose an algorithm, termed Scaled Fused Dantzig Selector (SFDS), that accomplishes the aforementioned learning task by means of a second-order cone program. A special emphasize is put on the particular instance of fused sparsity corresponding to the learning in presence of outliers. We establish finite sample risk bounds and carry out an experimental evaluation on both synthetic and real data.

AB - In this paper, we develop a novel approach to the problem of learning sparse representations in the context of fused sparsity and unknown noise level. We propose an algorithm, termed Scaled Fused Dantzig Selector (SFDS), that accomplishes the aforementioned learning task by means of a second-order cone program. A special emphasize is put on the particular instance of fused sparsity corresponding to the learning in presence of outliers. We establish finite sample risk bounds and carry out an experimental evaluation on both synthetic and real data.

M3 - Conference contribution

AN - SCOPUS:84877788363

SN - 9781627480031

T3 - Advances in Neural Information Processing Systems

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BT - Advances in Neural Information Processing Systems 25

T2 - 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012

Y2 - 3 December 2012 through 6 December 2012

ER -

Fused sparsity and robust estimation for linear models with unknown variance

Abstract

Publication series

Conference

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Cite this