Learning to Hash on Structured Data


  • Qifan Wang Purdue University
  • Luo Si Purdue University
  • Bin Shen Purdue University




Hashing, Structure Data, Similarity Search


Hashing techniques have been widely applied for large scale similarity search problems due to the computational and memory efficiency.However, most existing hashing methods assume data examples are independently and identically distributed.But there often exists various additional dependency/structure information between data examplesin many real world applications. Ignoring this structure information may limit theperformance of existing hashing algorithms.This paper explores the research problemof learning to Hash on Structured Data (HSD) and formulates anovel framework that considers additional structure information.In particular, the hashing function is learned in a unified learning framework by simultaneously ensuring the structural consistency and preserving the similarities between data examples.An iterative gradient descent algorithm is designed as the optimization procedure. Furthermore, we improve the effectiveness of hashing function through orthogonal transformation by minimizing the quantization error.Experimentalresults on two datasets clearly demonstrate the advantages ofthe proposed method over several state-of-the-art hashing methods.




How to Cite

Wang, Q., Si, L., & Shen, B. (2015). Learning to Hash on Structured Data. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9557



Main Track: Novel Machine Learning Algorithms