The efficient estimation of an approximate model order is very important for real applications with multi-dimensional data if the observed low-rank corrupted by additive noise. In this paper, we present a novel robust method noise-corrupted based on LineAr Regression Global Eigenvalues (LaRGE). LaRGE uses multi-linear singular values obtained from HOSVD measurement tensor to construct global ei...

evaporation is one of the main parameters for the optimum operation of reservoirs, design of irrigation systems and scientific management of water resources. accurate estimation of the water evaporation level is crucial in any region especially in arid and semiarid regions. in this study, the feasibility of simulation of pan evaporation in maraghe station using the multiple regression models we...

Hiroshi Murata, Linda M. Zangwill, Yuri Fujino, Masato Matsuura, Atsuya Miki, Kazunori Hirasawa, Masaki Tanito, Shiro Mizoue, Kazuhiko Mori, Katsuyoshi Suzuki, Takehiro Yamashita, Kenji Kashiwagi, Nobuyuki Shoji, and Ryo Asaoka Department of Ophthalmology, University of Tokyo Graduate School of Medicine, Tokyo, Japan Shiley Eye Institute Hamilton Glaucoma Center, University of California, San D...

Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables. It arises in several interesting industrial and environmental application domains, such as ecological modelling and energy forecasting. This paper presents an ensemble method for multi-target regression that constructs new target variables via r...

The multivariate multi-response (MVMR) linear regression problem is investigated, in which design matrices are Gaussian with covariance matrices Σ = ( Σ, . . . ,Σ ) for K linear regressions. The support union of K p-dimensional regression vectors (collected as columns of matrix B∗) are recovered using l1/l2-regularized Lasso. Sufficient and necessary conditions to guarantee successful recovery ...