Hiroyuki Kasai

Learn More
We propose a novel Riemannian manifold pre-conditioning approach for the tensor completion problem with rank constraint. A novel Rieman-nian metric or inner product is proposed that exploits the least-squares structure of the cost function and takes into account the structured symmetry that exists in Tucker decomposition. The specific metric allows to use(More)
We propose an online tensor subspace tracking algorithm based on the CP decomposition exploiting the recursive least squares (RLS), dubbed OnLine Low-rank Subspace tracking by TEnsor CP Decomposition (OLSTEC). Numerical evaluations show that the proposed OLSTEC algorithm gives faster convergence per iteration comparing with the state-of-the-art online(More)
This paper presents an environmental sound classification method that is noise-robust against sounds recorded by mobile devices, and presents evaluation of its performance. This method is specifically designed to recognize higher semantics of context from environmental sound. Conventionally, sound classifications have used acoustic features in the frequency(More)