Laplacian co-hashing of terms and documents

Authors:
Dell Zhang;Jun Wang;Deng Cai;Jinsong Lu
Affiliations:
School of Business, Economics and Informatics Birkbeck, University of London, London, UK;Department of Computer Science, University College London, London, UK;State Key Lab of CADSCG, College of Computer Science, Zhejiang University, China;School of Business, Economics and Informatics Birkbeck, University of London, London, UK
Venue:
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Year:
2010

Citing 4
Cited 6

Co-clustering documents and words using bipartite spectral graph partitioning

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Laplacian Eigenmaps for dimensionality reduction and data representation

Neural Computation
Introduction to Information Retrieval

Introduction to Information Retrieval
Semantic hashing

International Journal of Approximate Reasoning

Self-taught hashing for fast similarity search

Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Composite hashing with multiple information sources

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Learning binary codes for collaborative filtering

Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Rank hash similarity for fast similarity search

Information Processing and Management: an International Journal
Sparse hashing for fast multimedia search

ACM Transactions on Information Systems (TOIS)
Fitted spectral hashing

Proceedings of the 21st ACM international conference on Multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

A promising way to accelerate similarity search is semantic hashing which designs compact binary codes for a large number of documents so that semantically similar documents are mapped to similar codes within a short Hamming distance. In this paper, we introduce the novel problem of co-hashing where both documents and terms are hashed simultaneously according to their semantic similarities. Furthermore, we propose a novel algorithm Laplacian Co-Hashing (LCH) to solve this problem which directly optimises the Hamming distance.