Year: 2018
Type: conference-paper
Abstract: It is common in machine learning applications that unlabeled data are abundant while acquiring labels is extremely difficult. In order to reduce the cost of training model while maintaining the model quality, active learning provides a feasible solution. Instead of acquiring labels for random samples, active learning methods carefully select the data to be labeled so as to alleviate the impact from the redundancy or noise in the selected data and improve the trained model performance. In early stage experimental design, previous active learning methods adopted data reconstruction framework, such that the selected data maintained high representative power. However, these models did not consider the data class structure, thus the selected samples could be predominated by the samples from major classes. Such mechanism fails to include samples from the minor classes thus tends to be less "representative". To solve this challenging problem, we propose a novel active learning model for the early stage of experimental design. We use exclusive sparsity norm to enforce the selected samples to be (roughly) evenly distributed among different groups. We provide a new efficient optimization algorithm and theoretically prove the optimal convergence rate O(1/{T^2}). With a simple substitution, we reduce the computational load of each iteration from O(n^3) to O(n^2), which makes our algorithm more scalable than previous frameworks.
Authors: Xiaoqian Wang, Yijun Huang, Ji Liu, Heng Huang
Institutions: University of Pittsburgh, University of Rochester
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: ABI Innovation: A New Automated Data Integration, Annotations, and Interaction Network Inference System for Analyzing Drosophila Gene Expression, III: Medium: Collaborative Research: Robust Large-Scale Electronic Medical Record Data Mining Framework to Conduct Risk Stratification for Personalized Intervention, AF: Small: Collaborative Research: Personalized Environmental Monitoring of Type 1 Diabetes (T1D): A Dynamic System Perspective, G5520316217, BIGDATA: Collaborative Research: IA: Big Imaging-Omics Data Mining Framework for Precision Medicine, III: Small: Robust Large-Scale Data Mining for Knowledge Discovery in Depression Thought Records, SCH: EXP: Collaborative Research: Privacy-Preserving Framework for Publishing Electronic Healthcare Records, G9000211028, G9000243647
OpenAlex ID: W2808405490