  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.
