Following curved regularized optimization solution paths

Saharon Rosset

Following curved regularized optimization solution paths

^*Corresponding author for this work

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

Abstract

Regularization plays a central role in the analysis of modern data, where non-regularized fitting is likely to lead to over-fitted models, useless for both prediction and interpretation. We consider the design of incremental algorithms which follow paths of regularized solutions, as the regularization varies. These approaches often result in methods which are both efficient and highly flexible. We suggest a general path-following algorithm based on second-order approximations, prove that under mild conditions it remains "very close" to the path of optimal solutions and illustrate it with examples.

Original language	English
Title of host publication	Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004
Publisher	Neural information processing systems foundation
ISBN (Print)	0262195348, 9780262195348
State	Published - 2005
Externally published	Yes
Event	18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada Duration: 13 Dec 2004 → 16 Dec 2004

Publication series

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

Conference

Conference	18th Annual Conference on Neural Information Processing Systems, NIPS 2004
Country/Territory	Canada
City	Vancouver, BC
Period	13/12/04 → 16/12/04

Cite this

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title = "Following curved regularized optimization solution paths",

abstract = "Regularization plays a central role in the analysis of modern data, where non-regularized fitting is likely to lead to over-fitted models, useless for both prediction and interpretation. We consider the design of incremental algorithms which follow paths of regularized solutions, as the regularization varies. These approaches often result in methods which are both efficient and highly flexible. We suggest a general path-following algorithm based on second-order approximations, prove that under mild conditions it remains {"}very close{"} to the path of optimal solutions and illustrate it with examples.",

author = "Saharon Rosset",

year = "2005",

language = "אנגלית",

isbn = "0262195348",

series = "Advances in Neural Information Processing Systems",

publisher = "Neural information processing systems foundation",

booktitle = "Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004",

note = "18th Annual Conference on Neural Information Processing Systems, NIPS 2004 ; Conference date: 13-12-2004 Through 16-12-2004",

}

Rosset, S 2005, Following curved regularized optimization solution paths. in Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004. Advances in Neural Information Processing Systems, Neural information processing systems foundation, 18th Annual Conference on Neural Information Processing Systems, NIPS 2004, Vancouver, BC, Canada, 13/12/04.

Following curved regularized optimization solution paths. / Rosset, Saharon.
Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004. Neural information processing systems foundation, 2005. (Advances in Neural Information Processing Systems).

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

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Following curved regularized optimization solution paths

Abstract

Publication series

Conference

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Cite this