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Generalized Sparse Learning of Linear Models Over the Complete Subgraph Feature Set

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/68245

Title: Generalized Sparse Learning of Linear Models Over the Complete Subgraph Feature Set
Authors: Takigawa, Ichigaku Browse this author →KAKEN DB
Mamitsuka, Hiroshi Browse this author
Keywords: Supervised learning for graphs
graph mining
sparsity-inducing regularization
block coordinate gradient descent
simultaneous feature learning
Issue Date: Feb-2017
Publisher: IEEE Computer Society
Journal Title: IEEE transactions on pattern analysis and machine intelligence
Volume: 39
Issue: 3
Start Page: 617
End Page: 624
Publisher DOI: 10.1109/TPAMI.2016.2567399
Abstract: Supervised learning over graphs is an intrinsically difficult problem: simultaneous learning of relevant features from the complete subgraph feature set, in which enumerating all subgraph features occurring in given graphs is practically intractable due to combinatorial explosion. We show that 1) existing graph supervised learning studies, such as Adaboost, LPBoost, and LARS/LASSO, can be viewed as variations of a branch-and-bound algorithm with simple bounds, which we call Morishita-Kudo bounds; 2) We present a direct sparse optimization algorithm for generalized problems with arbitrary twice-differentiable loss functions, to which Morishita-Kudo bounds cannot be directly applied; 3) We experimentally showed that i) our direct optimization method improves the convergence rate and stability, and ii) L1-penalized logistic regression (L1-LogReg) by our method identifies a smaller subgraph set, keeping the competitive performance, iii) the learned subgraphs by L1-LogReg are more size-balanced than competing methods, which are biased to small-sized subgraphs.
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Type: article (author version)
URI: http://hdl.handle.net/2115/68245
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 瀧川 一学

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