Probabilistc methods are great in inferring latent representations from complex data. But, they often have some shortcomings in terms of sparsity control and computational cost. We have developed sparse topical coding, or STC, a non-probabilistic formulation of topic models. STC relaxes the normalization constraint of a probability distribution and thus effectively controls the sparsity of latent representations.

• STC: a topic model that learns sparse representations for words and documents