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We explore a general statistical framework for low-rank modeling of matrix-valued data, based on convex optimization with a generalized nuclear norm penalty. We study several related problems: the usual low-rank matrix completion problem with flexible loss functions arising from generalized linear models; reduced-rank regression and multi-task learning; and generalizations of both problems where side information about rows and columns is available, in the form of features or smoothing kernels. We show that our approach encompasses maximum a posteriori estimation arising from Bayesian hierarchical modeling with latent factors, and discuss ramifications of the missing-data mechanism in the context of matrix completion. While the above problems can be naturally posed as rank-constrained optimization problems, which are nonconvex and computationally difficult, we show how to relax them via generalized nuclear norm regularization to obtain convex optimization problems. We discuss algorithms drawing inspiration from modern convex optimization methods to address these large scale convex optimization computational tasks. Finally, we illustrate our flexible approach in problems arising in functional data reconstruction and ecological species distribution modeling. [ABSTRACT FROM AUTHOR]

Abstract: Flume experiments were carried out to study the turbulence and its impact on suspension and segregation of grain-sizes under unidirectional flow conditions over the sand-gravel mixture bed. The components of fluid velocity with fluctuations were measured vertically using 3-D Micro-acoustic Doppler velocimeter (ADV). The theoretical models for velocity and sediment suspension have been developed based on the concept of mixing length that includes the damping effect of turbulence due to sediment suspension in the flow over the sand-gravel mixture bed. Statistical analysis of segregation of grain-sizes along downstream of the bed has been performed using the principle of unsupervised learning or clustering problem. Exploratory data analysis suggests that there is a progressive downstream fining of sediment sizes with selective depositions of gravels, sand-gravels and sand materials along the stream, which may be segmented into three regions such as, the upstream, the transitional and the downstream respectively. This contribution is relevant to understand the direction of ancient rivers, the bed material character in the river form, sorting process and its role in controlling the sediment flux through landscape. [Copyright &y& Elsevier]