Learning from High-Dimensional Data in Multitask/Multilabel Classification

James T. Kwok

doi:10.1109/ACPR.2013.214

Learning from High-Dimensional Data in Multitask/Multilabel Classification

James T. Kwok^*

^*Corresponding author for this work

Department of Computer Science and Engineering

Research output: Contribution to conference › Conference Paper › peer-review

Abstract

Real-world data sets are highly complicated. They can contain a lot of features, and may involve multiple learning tasks with intrinsically or explicitly represented task relationships. In this paper, we briefly discuss several recent approaches that can be used in these scenarios. The algorithms presented are flexible in capturing the task relationships, computationally efficient with good scalability, and have better empirical performance than the existing approaches.

Original language	English
Pages	16-17
Number of pages	2
DOIs	https://doi.org/10.1109/ACPR.2013.214
Publication status	Published - 2013
Event	2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013 - Naha, Okinawa, Japan Duration: 5 Nov 2013 → 8 Nov 2013

Conference

Conference	2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013
Country/Territory	Japan
City	Naha, Okinawa
Period	5/11/13 → 8/11/13

Keywords

Multilabel learning
Multitask learning
Sparse modeling

Access to Document

10.1109/ACPR.2013.214

Cite this

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title = "Learning from High-Dimensional Data in Multitask/Multilabel Classification",

abstract = "Real-world data sets are highly complicated. They can contain a lot of features, and may involve multiple learning tasks with intrinsically or explicitly represented task relationships. In this paper, we briefly discuss several recent approaches that can be used in these scenarios. The algorithms presented are flexible in capturing the task relationships, computationally efficient with good scalability, and have better empirical performance than the existing approaches.",

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year = "2013",

doi = "10.1109/ACPR.2013.214",

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AU - Kwok, James T.

PY - 2013

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AB - Real-world data sets are highly complicated. They can contain a lot of features, and may involve multiple learning tasks with intrinsically or explicitly represented task relationships. In this paper, we briefly discuss several recent approaches that can be used in these scenarios. The algorithms presented are flexible in capturing the task relationships, computationally efficient with good scalability, and have better empirical performance than the existing approaches.

KW - Multilabel learning

KW - Multitask learning

KW - Sparse modeling

UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000339501000004

UR - https://openalex.org/W2538139864

UR - https://www.scopus.com/pages/publications/84899083889

U2 - 10.1109/ACPR.2013.214

DO - 10.1109/ACPR.2013.214

M3 - Conference Paper

SP - 16

EP - 17

T2 - 2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013

Y2 - 5 November 2013 through 8 November 2013

ER -

Learning from High-Dimensional Data in Multitask/Multilabel Classification

Abstract

Conference

Keywords

Access to Document

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