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2013 ICML ICML 2013

Predictable Dual-View Hashing

Abstract

We propose a Predictable Dual-View Hashing (PDH) algorithm which embeds proximity of data samples in the original spaces. We create a cross-view hamming space with the ability to compare information from previously incomparable domains with a notion of ‘predictability’. By performing comparative experimental analysis on two large datasets, PASCAL-Sentence and SUN-Attribute, we demonstrate the superiority of our method to the state-of-the-art dual-view binary code learning algorithms.

🚀 Conference Pioneer — ICML 2013
🧭 Keyword Pioneer — similarity preservation
🌉 Interdisciplinary Bridge — Computer Science and Machine Learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio

Authors

Mohammad Rastegari , Jonghyun Choi , Shobeir Fakhraei , Daume Hal , Larry Davis

Topics

Machine Learning > Core Methods > Metric Learning Machine Learning > Core Methods > Embedding Learning Machine Learning > Learning Types > Unsupervised Learning Computer Science > Applications > Information Retrieval

Keywords

similarity search hash function learning binary code similarity preservation dual-view hashing binary code learning cross-view hamming cross-view hamming space
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