Acceleration of stochastic approximation by averaging
SIAM Journal on Control and Optimization
Elements of artificial neural networks
Elements of artificial neural networks
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Solving large scale linear prediction problems using stochastic gradient descent algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
SVM optimization: inverse dependence on training set size
Proceedings of the 25th international conference on Machine learning
Sparse Online Learning via Truncated Gradient
The Journal of Machine Learning Research
Robust Stochastic Approximation Approach to Stochastic Programming
SIAM Journal on Optimization
Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization
The Journal of Machine Learning Research
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
The Journal of Machine Learning Research
Analysis of stochastic gradient algorithms for linear regression problems
IEEE Transactions on Information Theory
Hi-index | 0.00 |
The least squares problem is one of the most important regression problems in statistics, machine learning and data mining. In this paper, we present the Constrained Stochastic Gradient Descent (CSGD) algorithm to solve the large-scale least squares problem. CSGD improves the Stochastic Gradient Descent (SGD) by imposing a provable constraint that the linear regression line passes through the mean point of all the data points. It results in the best regret bound $O(\log{T})$, and fastest convergence speed among all first order approaches. Empirical studies justify the effectiveness of CSGD by comparing it with SGD and other state-of-the-art approaches. An example is also given to show how to use CSGD to optimize SGD based least squares problems to achieve a better performance.