In recent years, functional similarity has played an independent role in some biological fields such as gene clustering, gene functional prediction, and evaluation for proteinprotein interaction. In this premise, some effective methods have already been proposed based on Gene Ontology (GO). Although these mainstream methods achieve the purpose for measuring gene functional similarity, they may have some deficiency when calculating the Information Content (IC) of GO terms. Consequently, measuring the functional similarity accurately is still a meaningful objective of research. In this paper, a novel method called SWE, is proposed for measuring gene functional similarity based on the GO graph. Firstly, an algorithm to measure terms' semantics based on their information in the GO graph is put forward. The information of GO terms mainly contains their depth, ancestors and descendants. Secondly, we calculate the IC of a term set by means of retrieving the inherited relationship between terms in a term set. Finally, the functional similarity between two genes is computed based on the IC overlap ratio of term sets annotating two genes respectively. Results demonstrate that SWE is superior to existing methods in some experiments such as functional classification of genes in a biological pathway, protein-protein interaction and gene expression experiment. Further analysis demonstrates that SWE takes not only the specificity of terms into account, but their information in the GO graph, both of which are shown to be consistent with human perspectives. © 2020 IEEE.

Image clustering has been one of the most important issues in the field of pattern recognition. However, most of existing methods only focus on utilizing either content or context information of images, failing to consider both of them. In fact, the powerful algorithms can be realized by a combination of the rich content and context information. This paper proposes a novel content-context information bottleneck (C2IB) algorithm, which simultaneously explores and exploits the content and context information for discovering image clusters. The content describes the intrinsic characteristics contained in each image such as the appearance feature, and the context depicts the close correlations between images such as inter-image distance or similarity. Then, we formulate the problem as an information loss function by maximally preserving the content and context information while compressing the images. Finally, we design a new sequential method for the optimization. Experimental results show the superiority of the proposed method.
© 2020 IEEE.

Existing visual-textual cross-modal clustering techniques focus on finding a clustering partition of different modalities by dealing with each modality dependently or integrating multiple modalities into a shared space, which may results in unsatisfactory performance due to the heterogeneous gap of different modalities. Aiming at this problem, we propose a novel heterogeneous dual-task clustering (HDC) method, which is capable of exploring high-level relatedness between visual and textual data to improve the performance of individual task. Our intuition is that although the visual and textual data are heterogenous to each other, they may share related high-level semantics and rich latent correlations, which can lead to improved performance if we treat the clustering of visual and textual data as different but related learning tasks. Specifically, the problem of heterogeneous dual-task clustering is formulated as an information-theoretic function, in which the low-level information in each modality and high-level relatedness between multiple modalities are maximally preserved. Then, a progressive optimization method is proposed to ensure a local optimal solution. Extensive experiments show noticeable performance of the HDC approach in comparison with several state-of-the-art baselines. © 2020 by SIAM.