Paper Info
Title
Triply Stochastic Gradient Method For Large-Scale Nonlinear Similar Unlabeled Classification
Abstract
Similar unlabeled (SU) classification is pervasive in many real-world applications, where only similar data pairs (two data points have the same label) and unlabeled data points are available to train a classifier. Recent work has identified a practical SU formulation and has derived the corresponding estimation error bound. It evaluated SU learning with linear classifiers on medium-sized datasets. However, in practice, we often need to learn nonlinear classifiers on large-scale datasets for superior predictive performance. How this could be done in an efficient manner is still an open problem for SU classification. In this paper, we propose a scalable kernel learning algorithm for SU classification using a triply stochastic optimization framework, called TSGSU. Specifically, in each iteration, our method randomly samples an instance from the similar pairs set, an instance from the unlabeled set, and their random features to calculate the stochastic functional gradient for the model update. Theoretically, we prove that our method can converge to a stationary point at the rate of O(1/root T) after T iterations. Experiments on various benchmark datasets and highdimensional datasets not only demonstrate the scalability of TSGSU but also show the efficiency of TSGSU compared with existing SU learning algorithms while retaining similar generalization performance.
Year
DOI
Venue
2021
10.1007/s10994-021-05980-1
MACHINE LEARNING
Keywords
DocType
Volume
Weakly-supervised learning, SU classification, Kernel method, Large-scale optimization
Journal
110
Issue
ISSN
Citations 
8
0885-6125
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Shi Wanli101.35
Bin Gu264833.45
Xiang Li3528.31
Cheng Deng4128385.48
Heng Huang53080203.21