The plenty information from multiple views data as well as the complementary information among different views are usually beneficial to various tasks, e.g., clustering, classification, de-noising. Multi-view subspace clustering is based on the fact that the multi-view data are generated from a latent subspace. To recover the underlying subspace structure, the success of the sparse and/or low-r...

Principal component analysis (PCA) is possibly one of the most widely used statistical tools to recover a low-rank structure of the data. In the highdimensional settings, the leading eigenvector of the sample covariance can be nearly orthogonal to the true eigenvector. A sparse structure is then commonly assumed along with a low rank structure. Recently, minimax estimation rates of sparse PCA w...

We consider the task of learning the parameters of a single component of a mixture model, for the case when we are given side information about that component; we call this the “search problem” in mixture models. We would like to solve this with computational and sample complexity lower than solving the overall original problem, where one learns parameters of all components. Our main contributi...

Subspace clustering is an emerging task that aims at detecting clusters in entrenched in subspaces. Recent approaches fail to reduce results to relevant subspace clusters. Their results are typically highly redundant and lack the fact of considering the critical problem, “the density divergence problem,” in discovering the clusters, where they utilize an absolute density value as the density th...

Convective heat transfer and entropy generation in a 3D closed cavity, equipped with adiabatic-driven baffle and filled with CNT (carbon nanotube)-water nanofluid, are numerically investigated for a range of Rayleigh numbers from 103 to 105. This research is conducted for three configurations; fixed baffle (V = 0), rotating baffle clockwise (V+) and rotating baffle counterclockwise (V−) and a r...