738 research outputs found
Stochastic Optimization of PCA with Capped MSG
We study PCA as a stochastic optimization problem and propose a novel
stochastic approximation algorithm which we refer to as "Matrix Stochastic
Gradient" (MSG), as well as a practical variant, Capped MSG. We study the
method both theoretically and empirically
Iodine and Thyroid Cancer in Goa
There is a low papillary to follicular ratio in iodine deficient areas. A study of malignant thyroid tumors done over a period of 4 years in Goa shows that the ratio of papillary to follicular carcinoma in Goa conforms to a iodine deficient status of the population
Stochastic Optimization for Deep CCA via Nonlinear Orthogonal Iterations
Deep CCA is a recently proposed deep neural network extension to the
traditional canonical correlation analysis (CCA), and has been successful for
multi-view representation learning in several domains. However, stochastic
optimization of the deep CCA objective is not straightforward, because it does
not decouple over training examples. Previous optimizers for deep CCA are
either batch-based algorithms or stochastic optimization using large
minibatches, which can have high memory consumption. In this paper, we tackle
the problem of stochastic optimization for deep CCA with small minibatches,
based on an iterative solution to the CCA objective, and show that we can
achieve as good performance as previous optimizers and thus alleviate the
memory requirement.Comment: in 2015 Annual Allerton Conference on Communication, Control and
Computin
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