Date of Award
12-1-2012
Document Type
Campus Access Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Wei Ding
Second Advisor
Dan A. Simovici
Third Advisor
Marc Pomplun
Abstract
The least squares problem is one of the most important regression problems in statistics and machine learning. In this paper, we present an Averaging Projection Stochastic Gradient Descent (APSGD) algorithm to solve the large-scale least squares problem. APSGD improves the Stochastic Gradient Descent (SGD) by using the constraint that the linear regression line passes through the mean point of all the data points. It results in the best regret bound O(logT), and fastest convergence speed among all first order approaches. Empirical studies confirm the effectiveness of APSGD by comparing it with the state-of-art methods.
Recommended Citation
Mu, Yang, "Averaging Projected Stochastic Gradient Descent for Large Scale Square Problem" (2012). Graduate Masters Theses. 149.
https://scholarworks.umb.edu/masters_theses/149
Comments
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