2018

Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations

Aytekin, Caglar, Ni, Xingyang, Cricri, Francesco et al.

Understand

Clustering is essential to many tasks in pattern recognition and computer vision.

With the advent of deep learning, there is an increasing interest in learning deep unsupervised representations for clustering analysis.
Many works on this domain rely on variants of auto-encoders and use the encoder outputs as representations/features for clustering.
In this paper, we show that an l2 normalization constraint on these representations during auto-encoder training, makes the representations more separable and compact in the Euclidean space after training.

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