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arXiv · 2005.06632

SCAT: Second Chance Autoencoder for Textual Data

Somaieh Goudarzvand·Gharib Gharibi·Yugyung Lee
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Abstract

We present a k-competitive learning approach for textual autoencoders named Second Chance Autoencoder (SCAT). SCAT selects the $k$ largest and smallest positive activations as the winner neurons, which gain the activation values of the loser neurons during the learning process, and thus focus on retrieving well-representative features for topics. Our experiments show that SCAT achieves outstanding performance in classification, topic modeling, and document visualization compared to LDA, K-Sparse, NVCTM, and KATE.

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Somaieh Goudarzvand, Gharib Gharibi, Yugyung Lee. 2020-05-11. SCAT: Second Chance Autoencoder for Textual Data. https://arxiv.org/abs/2005.06632

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