Learning from Similarity-Confidence Data

2021/02/13 by Yuzhou Cao, Lei Feng, Cao, Yuzhou +9 · 4 citations
Computer Science · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.2102.06879

openalex publication_date 2021/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Weakly supervised learning has drawn considerable attention recently to reduce the expensive time and labor consumption of labeling massive data. In this paper, we investigate a novel weakly supervised learning problem of learning from similarity-confidence (Sconf) data, where we aim to learn an effective binary classifier from only unlabeled data pairs equipped with confidence that illustrates their degree of similarity (two examples are similar if they belong to the same class). To solve this problem, we propose an unbiased estimator of the classification risk that can be calculated from only Sconf data and show that the estimation error bound achieves the optimal convergence rate. To alleviate potential overfitting when flexible models are used, we further employ a risk correction scheme on the proposed risk estimator. Experimental results demonstrate the effectiveness of the proposed methods.

Citations

• Gradient-based learning applied to document recognition
• EMNIST: an extension of MNIST to handwritten letters
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Unbiased Risk Estimators Can Mislead: A Case Study of Learning with\n Complementary Labels
• Learning Multiple Layers of Features from Tiny Images

Cited by

• Learning from Uncertain Similarity and Unlabeled Data
• Learning from Similarity-Confidence and Confidence-Difference
• Learning from Hard Labels with Additional Supervision on Non-Hard-Labeled Classes
• A Unified Empirical Risk Minimization Framework for Flexible N-Tuples Weak Supervision

Related

• Learning from Similarity-Confidence and Confidence-Difference
• Learning from Uncertain Similarity and Unlabeled Data
• Classification from Pairwise Similarity and Unlabeled Data
• Binary Classification with Confidence Difference
• Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization