Large-scale Multi-label Learning with Missing Labels
The multi-label classification problem has generated significant interest in recent years. However, existing approaches do not adequately address two key challenges: (a) the ability to tackle problems with a large number (say millions) of labels, and (b) the ability to handle data with missing labels. In this paper, we directly address both these problems by studying the multi-label problem in a generic empirical risk minimization (ERM) framework. Our framework, despite being simple, is surprisingly able to encompass several recent label-compression based methods which can be derived as special cases of our method. To optimize the ERM problem, we develop techniques that exploit the structure of specific loss functions - such as the squared loss function - to offer efficient algorithms. We further show that our learning framework admits formal excess risk bounds even in the presence of missing labels. Our risk bounds are tight and demonstrate better generalization performance for low-rank promoting trace-norm regularization when compared to (rank insensitive) Frobenius norm regularization. Finally, we present extensive empirical results on a variety of benchmark datasets and show that our methods perform significantly better than existing label compression based methods and can scale up to very large datasets such as the Wikipedia dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
Missing LabelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningSimilar Papers 제목 키워드 기반
An Efficient Large-scale Semi-supervised Multi-label Classifier Capable of Handling Missing labels
Multi-label classification has received considerable interest in recent years. Multi-label classifiers have to address many problems including: handling large-scale datasets with many instances and a large set of labels,…
Dimensionality ReductionMissing LabelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAddressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking
The study of label noise in sound event recognition has recently gained attention with the advent of larger and noisier datasets. This work addresses the problem of missing labels, one of the big weaknesses of large audi…
Missing LabelsAn Effective Approach for Multi-label Classification with Missing Labels
Compared with multi-class classification, multi-label classification that contains more than one class is more suitable in real life scenarios. Obtaining fully labeled high-quality datasets for multi-label classification…
ClassificationMissing LabelsMulti-class ClassificationMulti-Label Classification+2Measuring Fairness in Large-Scale Recommendation Systems with Missing Labels
In large-scale recommendation systems, the vast array of items makes it infeasible to obtain accurate user preferences for each product, resulting in a common issue of missing labels. Typically, only items previously rec…
FairnessMissing LabelsRecommendation SystemsLarge-Scale Multi-Label Learning with Incomplete Label Assignments
Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches mainly focus on exploiting label correlati…
Missing LabelsMulti-Label Learning