Sparse adaptive multi-hyperplane machine

Khanh Nguyen, Trung Le, Vu Nguyen, Dinh Phung

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)

Abstract

The Adaptive Multiple-hyperplane Machine (AMM) was recently proposed to deal with large-scale datasets. However, it has no principle to tune the complexity and sparsity levels of the solution. Addressing the sparsity is important to improve learning generalization, prediction accuracy and computational speedup. In this paper, we employ the max-margin principle and sparse approach to propose a new Sparse AMM (SAMM). We solve the new optimization objective function with stochastic gradient descent (SGD). Besides inheriting the good features of SGD-based learning method and the original AMM, our proposed Sparse AMM provides machinery and flexibility to tune the complexity and sparsity of the solution, making it possible to avoid overfitting and underfitting. We validate our approach on several large benchmark datasets. We show that with the ability to control sparsity, the proposed Sparse AMM yields superior classification accuracy to the original AMM while simultaneously achieving computational speedup.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication20th Pacific-Asia Conference, PAKDD 2016, Proceedings Part I
EditorsJames Bailey, Latifur Khan, Takashi Washio, Gillian Dobbie, Joshua Zhexue Huang, Ruili Wang
Place of PublicationCham Switzerland
PublisherSpringer
Pages27-39
Number of pages13
ISBN (Electronic)9783319317533
ISBN (Print)9783319317526
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2016 - Auckland, New Zealand
Duration: 19 Apr 201622 Apr 2016
Conference number: 20th
http://pakdd16.wordpress.fos.auckland.ac.nz/
https://link.springer.com/book/10.1007/978-3-319-31753-3

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer
Volume9651
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2016
Abbreviated titlePAKDD 2016
CountryNew Zealand
CityAuckland
Period19/04/1622/04/16
Internet address

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