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ODHD: one-class brain-inspired hyperdimensional computing for outlier detection

Authors:

X. Sharon HuAuthors Info & Claims

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

Pages 43 - 48

https://doi.org/10.1145/3489517.3530395

Published: 23 August 2022 Publication History

Abstract

Outlier detection is a classical and important technique that has been used in different application domains such as medical diagnosis and Internet-of-Things. Recently, machine learning-based outlier detection algorithms, such as one-class support vector machine (OCSVM), isolation forest and autoencoder, have demonstrated promising results in outlier detection. In this paper, we take a radical departure from these classical learning methods and propose ODHD, an outlier detection method based on hyperdimensional computing (HDC). In ODHD, the outlier detection process is based on a P-U learning structure, in which we train a one-class HV based on inlier samples. This HV represents the abstraction information of all inlier samples; hence, any (testing) sample whose corresponding HV is dissimilar from this HV will be considered as an outlier. We perform an extensive evaluation using six datasets across different application domains and compare ODHD with multiple baseline methods including OCSVM, isolation forest, and autoencoder using three metrics including accuracy, F1 score and ROC-AUC. Experimental results show that ODHD outperforms all the baseline methods on every dataset for every metric. Moreover, we perform a design space exploration for ODHD to illustrate the tradeoff between performance and efficiency. The promising results presented in this paper provide a viable option and alternative to traditional learning algorithms for outlier detection.

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Cited By

Xu WKrainess EPayani ALatapie HParhi K(2025)IFODHD: Improved Feature Selection Based Outlier Detection using Hyperdimensional ComputingJournal of Signal Processing Systems10.1007/s11265-025-01946-xOnline publication date: 31-Jan-2025
https://doi.org/10.1007/s11265-025-01946-x
Hernández-Cano ANi YZou ZZakeri AImani M(2024)Hyperdimensional computing with holographic and adaptive encoderFrontiers in Artificial Intelligence10.3389/frai.2024.13719887Online publication date: 9-Apr-2024
https://doi.org/10.3389/frai.2024.1371988
Ni YChung WCho SZou ZImani M(2024)Efficient Exploration in Edge-Friendly Hyperdimensional Reinforcement LearningProceedings of the Great Lakes Symposium on VLSI 202410.1145/3649476.3658760(111-118)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3649476.3658760
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cover image ACM Conferences

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

July 2022

1462 pages

ISBN:9781450391429

DOI:10.1145/3489517

General Chair:
Rob Oshana
NXP

Copyright © 2022 ACM.

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Publication History

Published: 23 August 2022

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DAC '22: 59th ACM/IEEE Design Automation Conference

July 10 - 14, 2022

California, San Francisco

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

Xu WKrainess EPayani ALatapie HParhi K(2025)IFODHD: Improved Feature Selection Based Outlier Detection using Hyperdimensional ComputingJournal of Signal Processing Systems10.1007/s11265-025-01946-xOnline publication date: 31-Jan-2025
https://doi.org/10.1007/s11265-025-01946-x
Hernández-Cano ANi YZou ZZakeri AImani M(2024)Hyperdimensional computing with holographic and adaptive encoderFrontiers in Artificial Intelligence10.3389/frai.2024.13719887Online publication date: 9-Apr-2024
https://doi.org/10.3389/frai.2024.1371988
Ni YChung WCho SZou ZImani M(2024)Efficient Exploration in Edge-Friendly Hyperdimensional Reinforcement LearningProceedings of the Great Lakes Symposium on VLSI 202410.1145/3649476.3658760(111-118)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3649476.3658760
Chen WWong HAchour SDe V(2024)Bitwise Adaptive Early Termination in Hyperdimensional Computing InferenceProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3656532(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3656532
Wang RMoon SHu XJiao XReis D(2024)A Computing-in-Memory-Based One-Class Hyperdimensional Computing Model for Outlier DetectionIEEE Transactions on Computers10.1109/TC.2024.337178273:6(1559-1574)Online publication date: Jun-2024
https://doi.org/10.1109/TC.2024.3371782
Ni YYang YChen HWang XLesica NZeng FImani M(2024)Hyperdimensional Brain-Inspired Learning for Phoneme Recognition With Large-Scale Inferior Colliculus Neural ActivitiesIEEE Transactions on Biomedical Engineering10.1109/TBME.2024.340827971:11(3098-3110)Online publication date: Nov-2024
https://doi.org/10.1109/TBME.2024.3408279
Komarraju SMejri MChatterjee ANatarajan SGoteti P(2024)TEACH: Outlier Oriented Testing of Analog/Mixed-Signal Circuits Using One-class Hyperdimensional Clustering2024 IEEE International Test Conference (ITC)10.1109/ITC51657.2024.00065(426-435)Online publication date: 3-Nov-2024
https://doi.org/10.1109/ITC51657.2024.00065
Ni YAbraham DIssa MHernández-Cano AImani MMercati PImani M(2024)Dynamic MAC Protocol for Wireless Spectrum Sharing via Hyperdimensional Self-LearningIEEE Access10.1109/ACCESS.2024.346486812(138519-138534)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3464868
Ge LMcInnes AWidge AParhi K(2024)Determining the Number of Clusters in Clinical Response of TMS Treatment using Hyperdimensional ComputingJournal of Signal Processing Systems10.1007/s11265-024-01921-y96:8-9(509-523)Online publication date: 26-Jun-2024
https://doi.org/10.1007/s11265-024-01921-y
Ma DZhang SJiao XTakahashi A(2023)Robust Hyperdimensional Computing against Cyber Attacks and Hardware ErrorsProceedings of the 28th Asia and South Pacific Design Automation Conference10.1145/3566097.3568355(598-605)Online publication date: 16-Jan-2023
https://dl.acm.org/doi/10.1145/3566097.3568355
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