Learning representations of ultrahigh-dimensional data for random distance-based outlier detection

Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods to address the curse of dimensionality. However, existing unsupervised representation learning methods mainly focus on pr...

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Main Authors: PANG, Guansong, CAO, Longbing, CHEN, Ling, LIAN, Defu, LIU, Huan
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/7141
https://ink.library.smu.edu.sg/context/sis_research/article/8144/viewcontent/11692_Article_Text_15220_1_2_20201228.pdf
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spelling sg-smu-ink.sis_research-81442022-04-22T04:21:40Z Learning representations of ultrahigh-dimensional data for random distance-based outlier detection PANG, Guansong CAO, Longbing CHEN, Ling LIAN, Defu LIU, Huan Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods to address the curse of dimensionality. However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i.e., outliers).This paper introduces a ranking model-based framework, called RAMODO, to address this issue. RAMODO unifies representation learning and outlier detection to learn low-dimensional representations that are tailored for a state-of-the-art outlier detection approach - the random distance-based approach. This customized learning yields more optimal and stable representations for the targeted outlier detectors. Additionally, RAMODO can leverage little labeled data as prior knowledge to learn more expressive and application-relevant representations. We instantiate RAMODO to an efficient method called REPEN to demonstrate the performance of RAMODO.Extensive empirical results on eight real-world ultrahigh dimensional data sets show that REPEN (i) enables a random distance-based detector to obtain significantly better AUC performance and two orders of magnitude speedup; (ii) performs substantially better and more stably than four state-of-the-art representation learning methods; and (iii) leverages less than 1% labeled data to achieve up to 32% AUC improvement. 2018-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7141 info:doi/10.1145/3219819.3220042 https://ink.library.smu.edu.sg/context/sis_research/article/8144/viewcontent/11692_Article_Text_15220_1_2_20201228.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Data Storage Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
Data Storage Systems
spellingShingle Databases and Information Systems
Data Storage Systems
PANG, Guansong
CAO, Longbing
CHEN, Ling
LIAN, Defu
LIU, Huan
Learning representations of ultrahigh-dimensional data for random distance-based outlier detection
description Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods to address the curse of dimensionality. However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i.e., outliers).This paper introduces a ranking model-based framework, called RAMODO, to address this issue. RAMODO unifies representation learning and outlier detection to learn low-dimensional representations that are tailored for a state-of-the-art outlier detection approach - the random distance-based approach. This customized learning yields more optimal and stable representations for the targeted outlier detectors. Additionally, RAMODO can leverage little labeled data as prior knowledge to learn more expressive and application-relevant representations. We instantiate RAMODO to an efficient method called REPEN to demonstrate the performance of RAMODO.Extensive empirical results on eight real-world ultrahigh dimensional data sets show that REPEN (i) enables a random distance-based detector to obtain significantly better AUC performance and two orders of magnitude speedup; (ii) performs substantially better and more stably than four state-of-the-art representation learning methods; and (iii) leverages less than 1% labeled data to achieve up to 32% AUC improvement.
format text
author PANG, Guansong
CAO, Longbing
CHEN, Ling
LIAN, Defu
LIU, Huan
author_facet PANG, Guansong
CAO, Longbing
CHEN, Ling
LIAN, Defu
LIU, Huan
author_sort PANG, Guansong
title Learning representations of ultrahigh-dimensional data for random distance-based outlier detection
title_short Learning representations of ultrahigh-dimensional data for random distance-based outlier detection
title_full Learning representations of ultrahigh-dimensional data for random distance-based outlier detection
title_fullStr Learning representations of ultrahigh-dimensional data for random distance-based outlier detection
title_full_unstemmed Learning representations of ultrahigh-dimensional data for random distance-based outlier detection
title_sort learning representations of ultrahigh-dimensional data for random distance-based outlier detection
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/7141
https://ink.library.smu.edu.sg/context/sis_research/article/8144/viewcontent/11692_Article_Text_15220_1_2_20201228.pdf
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