Аннотация
Deep neural networks have gained tremendous success in a broad range of
machine learning tasks due to its remarkable capability to learn semantic-rich
features from high-dimensional data. However, they often require large-scale
labelled data to successfully learn such features, which significantly hinders
their adaption into unsupervised learning tasks, such as anomaly detection and
clustering, and limits their applications into critical domains where obtaining
massive labelled data is prohibitively expensive. To enable downstream
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 theoretical
proven approach to obtain approximately preserved distances. To well predict
these random distances, the representation learner is optimised to learn
genuine class structures that are implicitly embedded in the randomly projected
space. Experimental results on 19 real-world datasets show our learned
representations substantially outperform state-of-the-art competing methods in
both anomaly detection and clustering tasks.
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