Learning complex-valued latent filters with absolute cosine similarity

We propose a new sparse coding technique based on the power mean of phase-invariant cosine distances. Our approach is a generalization of sparse filtering and K-hyperlines clustering. It offers a better sparsity enforcer than the L
₁
/L
₂
norm ratio that is typically used in sparse filtering. At the same time, the proposed approach scales better than the clustering counterparts for high-dimensional input. Our algorithm fully exploits the prior information obtained by preprocessing the observed data with whitening via an efficient row-wise decoupling scheme. In our simulating experiments, the algorithm produces better estimates than previous approaches do. It yields better separation of live recorded speech mixtures as well.