Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

1-2021

Abstract

Deep neural networks have gained great success in a broad range of tasks due to its remarkable capability to learn semantically rich features from high-dimensional data. However, they often require large-scale labelled data to successfully learn such features, which significantly hinders their adaption in unsupervised learning tasks, such as anomaly detection and clustering, and limits their applications to critical domains where obtaining massive labelled data is prohibitively expensive. To enable unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected space. Random mapping is a theoretically proven approach to obtain approximately preserved distances. To well predict these distances, the representation learner is optimised to learn genuine class structures that are implicitly embedded in the randomly projected space. Empirical results on 19 real-world datasets show that our learned representations substantially outperform a few state-of-theart methods for both anomaly detection and clustering tasks. Code is available at: https://git.io/ RDP

Discipline

Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

Proceedings of the 29th International Joint Conference on Artificial Intelligence IJCAI 2020, Virtual Conference, January 7-15

First Page

2950

Last Page

2956

Identifier

10.24963/ijcai.2020/408

Publisher

IJCAI

City or Country

Virtual Conference

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