On extensions of LARS by information geometry: Convex objectives and ̀p-norm

Masahiro Yukawa; Shun Ichi Amari

On extensions of LARS by information geometry: Convex objectives and ̀p-norm

研究成果: Paper › 査読

抄録

This paper addresses extensions of the Least Angle Regression (LARS) algorithm from two different aspects: (i) from quadratic to more general objectives, and (ii) from ̀1-norm to ̀p-norm for p < 1. The equiangular vector, which is the key of LARS, is reproduced in connection with the Riemannian metric induced by the objective function, thereby making the extensions feasible. It is shown, in the case of p < 1, that two types of trajectory . the c-trajectory and the λ-trajectory . need to be distinguished by revealing the discontinuity of the λ-trajectory.

本文言語	English
ページ	326-331
ページ数	6
出版ステータス	Published - 2011 12月 1
外部発表	はい
イベント	Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011, APSIPA ASC 2011 - Xi'an, China 継続期間: 2011 10月 18 → 2011 10月 21

Other

Other	Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011, APSIPA ASC 2011
国/地域	China
City	Xi'an
Period	11/10/18 → 11/10/21

ASJC Scopus subject areas

情報システム
信号処理

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On extensions of LARS by information geometry: Convex objectives and ̀p-norm

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