Learning for efficient retrieval of structured data with noisy queries

Authors:
Charles Parker;Alan Fern;Prasad Tadepalli
Affiliations:
Oregon State University, Corvallis, OR;Oregon State University, Corvallis, OR;Oregon State University, Corvallis, OR
Venue:
Proceedings of the 24th international conference on Machine learning
Year:
2007

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Abstract

Increasingly large collections of structured data necessitate the development of efficient, noise-tolerant retrieval tools. In this work, we consider this issue and describe an approach to learn a similarity function that is not only accurate, but that also increases the effectiveness of retrieval data structures. We present an algorithm that uses functional gradient boosting to maximize both retrieval accuracy and the retrieval efficiency of vantage point trees. We demonstrate the effectiveness of our approach on two datasets, including a moderately sized real-world dataset of folk music.