TY - UNPB

T1 - Spectral Algorithm for Low-rank Multitask Regression

AU - Gigi, Y.

AU - Wiesel, A.

AU - Nevo, S.

AU - Elidan, G.

AU - Hassidim, A.

AU - Matias, Y.

PY - 2019/10/27

Y1 - 2019/10/27

N2 - Multitask learning, i.e. taking advantage of the relatedness of individual tasks in order to improve performance on all of them, is a core challenge in the field of machine learning. We focus on matrix regression tasks where the rank of the weight matrix is constrained to reduce sample complexity. We introduce the common mechanism regression (CMR) model which assumes a shared left low-rank component across all tasks, but allows an individual per-task right low-rank component. This dramatically reduces the number of samples needed for accurate estimation. The problem of jointly recovering the common and the local components has a non-convex bi-linear structure. We overcome this hurdle and provide a provably beneficial non-iterative spectral algorithm. Appealingly, the solution has favorable behavior as a function of the number of related tasks and the small number of samples available for each one. We demonstrate the efficacy of our approach for the challenging task of remote river discharge estimation across multiple river sites, where data for each task is naturally scarce. In this scenario sharing a low-rank component between the tasks translates to a shared spectral reflection of the water, which is a true underlying physical model. We also show the benefit of the approach on the markedly different setting of image classification where the common component can be interpreted as the shared convolution filters.

AB - Multitask learning, i.e. taking advantage of the relatedness of individual tasks in order to improve performance on all of them, is a core challenge in the field of machine learning. We focus on matrix regression tasks where the rank of the weight matrix is constrained to reduce sample complexity. We introduce the common mechanism regression (CMR) model which assumes a shared left low-rank component across all tasks, but allows an individual per-task right low-rank component. This dramatically reduces the number of samples needed for accurate estimation. The problem of jointly recovering the common and the local components has a non-convex bi-linear structure. We overcome this hurdle and provide a provably beneficial non-iterative spectral algorithm. Appealingly, the solution has favorable behavior as a function of the number of related tasks and the small number of samples available for each one. We demonstrate the efficacy of our approach for the challenging task of remote river discharge estimation across multiple river sites, where data for each task is naturally scarce. In this scenario sharing a low-rank component between the tasks translates to a shared spectral reflection of the water, which is a true underlying physical model. We also show the benefit of the approach on the markedly different setting of image classification where the common component can be interpreted as the shared convolution filters.

UR - http://scholar.google.com/scholar?num=3&hl=en&lr=&q=allintitle%3A%20Spectral%20Algorithm%20for%20Low-rank%20Multitask%20Regression%2C%20author%3AGigi%20OR%20author%3AWiesel%20OR%20author%3ANevo%20OR%20author%3AElidan%20OR%20author%3AHassidim%20OR%20author%3AMatias&as_ylo=2019&as_yhi=&btnG=Search&as_vis=0

M3 - نسخة اولية

VL - 12204

T3 - arXiv preprint arXiv:1910.,

SP - 1

EP - 23

BT - Spectral Algorithm for Low-rank Multitask Regression

ER -