BCTH : A Novel Text Hashing Approach via B ayesian Clustering (2020)

Abstract: Similarity search is to find the most similar items for a certain target item. The ability of similarity search at large scale plays a significant role in many information retrieval applications, and thus has received much attention. Text hashing is a promising strategy, which utilizes binary encoding to represent documents, obtaining attractive performance. This paper makes the first attempt to utilize Bayesian Clustering for Text Hashing, dubbed as BCTH. Specifically, BCTH is able to map documents to binary codes by utilizing multiple Bayesian Clusterings in parallel, where each Bayesian Clustering is responsible for one bit. Our approach employs the bit-balanced constraint to maximize the amount of information in each bit. Meanwhile, the bit-uncorrected constraint is adopted to keep the independence among all bits. The time complexity of BCTH is linear, where the hash codes and hash function are jointly learned. The experimental results, based on four widely-used datasets, demonstrate that BCTH is competitive, compared with currently competitive baselines in the perspective of both precision and training speed.

## introduction the task of similarity search, also called nearest neighbor search, aims to find the most similar objects for a given query item @xcite . it plays a significant role in many information retrieval applications, such as document clustering, content-based retrieval, collaborative filtering @xcite , etc. with the development of many intelligent terminals, massive textual data has been produced over the past several decades. huge challenges exist in applying text similarity algorithms @xcite to large-scale corpora, since these ⇤ means the corresponding author. methods require complicated numerical computation. text hashing @xcite is a promising strategy and has obtained much attention. it maps semantically similar documents to hash codes with similar semantics through designing binary codes in a low-dimensional space. a hashing representation of each document usually needs only a few bits to be stored. the calculation of the similarity between two hash codes can be executed by a bit-wise xor operation. therefore, text hashing is an effective strategy to accelerate similarity queries and reduce data storage. most of the traditional text hashing methods consist of two stages @xcite @xcite . the first step is to learn hash code, preserving similarity among neighbors. then the hash function is trained through the self-taught method, with the text features and hash codes as the input @xcite . however, for m documents, o(m 2 motivated by the above observations, this paper attempts to utilize bayesian clustering for text hashing, dubbed as bcth. specifically, bcth can map documents to binary codes by using multiple bayesian clusterings in parallel, where each bayesian clustering is responsible for one bit. our approach employs the bit-balanced constraint to maximize the amount of information in each bit. meanwhile, the bit-uncorrelated constraint is adopted to keep independence among all bits. experimental results prove that our approach is competitive in the perspective of both precision and training speed. our contributions are summarized as follows: ## model the approach of our proposed bcth is introduced in this section. as is shown in fig. 1 , bcth is a general learning idea, which utilizes bayesian clustering that is based on the latent factor framework in text hashing. bcth can map documents to binary codes by using multiple bayesian clusterings in parallel, where each bayesian clustering is responsible for one bit. during this process, the bit-balanced constraint is to maximize the amount of information in each bit. meanwhile, the bituncorrelated constraint is adopted to keep independence among all bits. ## related work hashing methods can be divided into dataindependent methods and data-dependent methods @xcite . the well-known dataindependent methods include locality sensitive hashing (lsh) @xcite and its variants. data-dependent hashing methods are also known as learning to hash (l2h) methods by learning a hash function from data @xcite . at present, the main l2h methods @xcite ) can be divided into three categories: pairwise similarity preserving, multiwise similarity preserving, and implicit similarity preserving. the pairwise similarity-preserving methods aim to build a pairwise similarity matrix between two points, such as spectral hashing (sh) @xcite , hashing with graphs (agh) @xcite , discrete graph hashing (dgh) @xcite , fast supervised hashing (fasth) @xcite and column-sampling-based discrete supervised hashing (cosdish) @xcite . the multiwise similarity-preserving is similar to pairwise similarity, which uses three or more samples as a group to define generalized similarity measures @xcite . the implicit similarity-preserving methods maintain the similarity in an equivalent manner that adopts the idea of preserving the similarity of local neighbors @xcite . compared with this line of works, although our work also focuses on the nearest neighbor search, our work is different from theirs since (1) most of these works focus on images data, and (2) bayesian clustering is not covered in these works. another line of works discuss text hashing, is related to our work since our work also aims to learn binary code from documents effectively. for example, @xcite presented the self-taught hashing (sth) method for efficiently learning semantic hashing. @xcite incorporated both the tag information and the similarity information from probabilistic topic modeling. however, many of these models rely on pairwise similaritypreserving technique, which the time complexity is unavoidable o(m 2 ## conclusion this paper presents a general learning framework that utilizes multiple bayesian clusterings jointly for text hashing. we introduce two constraints to make the hash code effectively. specifically, the bit-balanced constraint is employed to maximize the amount of information in each bit, and the bituncorrelated constraint is adopted to keep the independence among all bits. the time complexity of our method is linear. based on four widelyused datasets, the experiment results demonstrate that bcth is competitive compared with current competitive baselines from the perspective of both precision and training speed.