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Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning
weishi shi · Qi Yu

Wed Jun 12 04:20 PM -- 04:25 PM (PDT) @ Room 201

Active learning for multi-label classification poses fundamental challenges given the complex label correlations and a potentially large and sparse label space. We propose a novel CS-BPCA process that integrates compressed sensing and Bayesian principal component analysis to perform a two-level label transformation, resulting in an optimally compressed continuous target space. Besides leveraging correlation and sparsity of a large label space for effective compression, an optimal compressing rate and the relative importance of the resultant targets are automatically determined through Bayesian inference. Furthermore, the orthogonality of the transformed space completely decouples the correlations among targets, which significantly simplifies multi-label sampling in the target space. We define a novel sampling function that leverages a multi-output Gaussian Process (MOGP). Gradient-free optimization strategies are developed to achieve fast online hyper-parameter learning and model retraining for active learning. Experimental results over multiple real-world datasets and comparison with competitive multi-label active learning models demonstrate the effectiveness of the proposed framework.

Author Information

weishi shi (Rochester Institute of Technology)

My research focuses on Data mining, machine learning and active learning. The contribution of my work including multi-class and multi-label active learning in knowledge-rich domains. Publications are listed below: 1.Correlation-aware multi-label active learning for web service tag recommendation (2017ICWS) 2.Statistical Learning of Domain-Specific Quality-of-Service Features from User Reviews (TOIT) 3.An Efficient Many-Class Active Learning Framework for Knowledge-Rich Domains.(2018ICDM) 4.From Novice to Expert Narratives of Dermatological Disease (co-author)(2018 IEEE International Conference on Pervasive Computing and Communications)

Qi Yu (Rochester Institute of Technology)

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