2009
NIPS
NeurIPS 2009
Regularized Distance Metric Learning:Theory and Algorithm
Abstract
In this paper, we examine the generalization error of regularized distance metric learning. We show that with appropriate constraints, the generalization error of regularized distance metric learning could be independent from the dimensionality, making it suitable for handling high dimensional data. In addition, we present an efficient online learning algorithm for regularized distance metric learning. Our empirical studies with data classification and face recognition show that the proposed algorithm is (i) effective for distance metric learning when compared to the state-of-the-art methods, and (ii) efficient and robust for high dimensional data.
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Trend Setter
— Supervised Learning
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Keyword Pioneer
— online learning algorithm
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Hot Topic Early Bird
— online learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
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Interdisciplinary Bridge
— Computer Vision and Machine Learning
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Metric Learning
Computer Vision > Analysis > Face Recognition
Machine Learning > Learning Types > Online Learning
Machine Learning > Learning Types > Supervised Learning
Machine Learning > Learning Paradigms > Online Learning