Hashing Representation Learning For Massive Heterogeneous Data

With the rapid growth of heterogeneous hashing,the efficiency of search between heterogeneous data become the bottleneck.This paper proposes to introduce hashing representation to speed up the search process.However,traditional hashing methods cannot model the heterogeneity of data.So,they cannot be applied to heterogeneous similarity search.This paper develops different hashing representation learning algorithms for corresponding heterogeneity,including feature heterogeneity,object heterogneity and network heterogneity,and achieve efficient and accurate similarity search in heterogeneous data.Below are the main contributions of this paper:1.Hashing representation learning for heterogeneous features: According to the high-dimensional,sparse and binary properties of attribute feature and lowdimensional,continuous and dense properties of content feature,we propose two specific probabilistic generative model for the two features respectively.Then,we can integrate the information from two types of feature,and make the hashing methods achieve higher accuracy.2.Hashing representation learning for heterogeneous objects: we propose Relation-aware Heterogeneous Hashing(Ra HH)for efficient cross-domain similarity search.We project heterogeneous data domain into different Hamming space,and learn the mapping between Hamming spaces with heterogeneous relations.Then,we can not only preserve the characteristics of different data domains,but also implement cross-domain similarity search.3.Hashing representation learning for heterogeneous networks: this paper focus on non-transitive network and directed network.We propose Multi-component Hashing and High-order Proximity Preserving Embedding to model the two types of network.Then,we can achieve efficient similarity search on heterogeneous networks.