Hashing technology has exhibited great cross-modal retrieval potential due to its appealing retrieval efficiency and storage effectiveness. Most current supervised cross-modal retrieval methods heavily rely on accurate semantic supervision, which is intractable for annotations with ever-growing sample sizes. By comparison, the existing unsupervised methods rely on accurate sample similarity preservation strategies with intensive computational costs to compensate for the lack of semantic guidance, which causes these methods to lose the power to bridge the semantic gap. Furthermore, both kinds of approaches need to search for the nearest samples among all samples in a large search space, whose process is laborious. To address these issues, this paper proposes an unsupervised dual deep hashing (UDDH) method with semantic-index and content-code for cross-modal retrieval. Deep hashing networks are utilized to extract deep features and jointly encode the dual hashing codes in a collaborative manner with a common semantic index and modality content codes to simultaneously bridge the semantic and heterogeneous gaps for cross-modal retrieval. The dual deep hashing architecture, comprising the head code on semantic index and tail codes on modality content, enhances the efficiency for cross-modal retrieval. A query sample only needs to search for the retrieved samples with the same semantic index, thus greatly shrinking the search space and achieving superior retrieval efficiency. UDDH integrates the learning processes of deep feature extraction, binary optimization, common semantic index, and modality content code within a unified model, allowing for collaborative optimization to enhance the overall performance. Extensive experiments are conducted to demonstrate the retrieval superiority of the proposed approach over the state-of-the-art baselines.

Current cross-modal retrieval methods heavily rely on accurate semantic labels or sample similarity measurements, and need to search for the nearest samples among all samples in the huge search space, severely limiting the application in stratifying large-scale and high-dimensional multimodal data. To tackle with the issues, this paper proposes an unsupervised cross-modal retrieval method to bypass the semanticwise supervision and samplewise similarity from a standpoint of featurewise matching, named by unsupervised dual hashing coding (UDC). It jointly learns the dual hashing codes on semantic tagging and sample content through factorizing a feature matching potential, which is allowed to bridge the semantic and heterogeneous gaps among different modalities simultaneously through maintaining the inter-modality-consistent semantic information and cross-modality-correlated sample content. In this way, each sample is uniquely coded by a head code on semanticwise tags, and tail codes on samplewise content. The dual coding design makes it very efficient for sample retrieval, in which the query sample only need to search for the retrieved ones with the same semantic tag, greatly narrowing down the search space. The proposed model avoids the calculation of massive sample-wise similarity and works with dual hashing coding scheme, which achieves a twofold efficiency enhancement for analyzing the large-scale and high-dimensional multimodal data. Extensive experiments have been conducted to demonstrate that it achieved superiority on computational time and retrieval performance.

Unsupervised feature selection is a vital yet challenging topic for effective data learning. Recently, 2-D feature selection methods show good performance on image analysis by utilizing the structure information of image. Current 2-D methods usually adopt a sparse regularization to spotlight the key features. However, such scheme introduces additional hyperparameter needed for pruning, limiting the applicability of unsupervised algorithms. To overcome these challenges, we design a feature filter to estimate the weight of image features for unsupervised feature selection. Theoretical analysis shows that a sparse regularization can be derived from the feature filter by transformation, indicating that the filter plays the same role as the popular sparse regularization does. We deploy two distinct strategies in terms of feature selection, called multiple feature filters and single common feature filter. The former divides the optimization problem into multiple independent subproblems and selects features that meet the respective interests of each subproblem. The latter selects features that are in the interest of the overall optimization problem. Extensive experiments on seven benchmark datasets show that our unsupervised 2-D weight-based feature selection methods achieve superior performance over the state-of-the-art methods.

Multi-view clustering divides data into their under-lying partitions by exploiting multiple views information. Popular approaches leverage cross-view information by self-expressive tensor learning and then learn a low-rank or sparse essential representation tensor for capturing the global structure of multi-view data. However, this process may encounter instability due to the lack of protection for local within-view structures. To overcome this problem, this paper proposes a unified L ow-rank and HyperGraph Laplacian regularized Tensor learning (LHGT) method for multi-view clustering, which aims to integrate within-view high-order affinities in self-expressive tensor learning for capturing inherent clustering structure. LHGT effectively extracts global cross-view and local within-view high-order statistics. An effective optimization procedure is tailored for the proposed model. Experimental results on six real-world datasets illustrate the efficacy of LHGT, where a clear advance over nine state-of-the-art approaches.

Multiple modality clustering seeks to partition objects via leveraging cross-modality relations to provide comprehensive descriptions of the same objects. Current clustering methods rely heavily on accurate affinity measurements among samples. The samplewise affinity is costive to be constructed yet easy to corrupt by the heterogeneous gap. In the era of big data, fast and accurate clustering of multiple modality data remains challenging. To fill the gap, we propose a novel approach to achieve the clustering by focusing on feature matching across different modalities instead of samplewise affinity. First, a feature matching matrix is calculated by measuring the potential featurewise correlations. The obtained matching matrix is decomposed into two bases corresponding to the column and row spaces of feature matching, acting as coded bases within feature spaces of the different modalities. Then, the sample assignment is obtained by jointly reconstructing the samples by the two bases. The feature matching potential and sample assignment are collaboratively learned by an alternating optimization scheme. The proposed method dramatically reduces the computational cost by avoiding the costive samplewise affinity estimation, without sacrificing accuracy. Extensive experiments on the synthetic and real-world datasets demonstrate its superior speed and high accuracy.

Multiview clustering seeks to partition objects via leveraging cross-view relations to provide a comprehensive description of the same objects. Most existing methods assume that different views are linear transformable or merely sampling from a common latent space. Such rigid assumptions betray reality, thus leading to unsatisfactory performance. To tackle the issue, we propose to learn both common and specific sampling spaces for each view to fully exploit their collaborative representations. The common space corresponds to the universal self-representation basis for all views, while the specific spaces are the view-specific basis accordingly. An iterative self-supervision scheme is conducted to strengthen the learned affinity matrix. The clustering is modeled by a convex optimization. We first solve its linear formulation by the popular scheme. Then, we employ the deep autoencoder structure to exploit its deep nonlinear formulation. The extensive experimental results on six real-world datasets demonstrate that the proposed model achieves uniform superiority over the benchmark methods.

Multi-omics data clustering aims at assigning elements to their respective categories via exploiting the complementary information among multi-omics samples, with the widely application on data mining, bioinformatics, biomedicine and so on. Current multi-omics data clustering methods rely heavily on accurate sample-wise affinity estimation, which is limited by large heterogeneous gap among different sampling spaces. In this paper, we propose a novelly collaborative clustering method, which measures feature-wise matching instead of sample-wise affinity for sample assignment via the guidance of the highly matched features. The method consists of three modules. Firstly, the feature matching is learned by feature-wise correlation. Secondly, the feature matching matrix is factorized into two coded bases within yielded feature spaces. Thirdly, the sample assignment is achieved by joint sample reconstruction employing the two bases. The sample-wise assignment and feature-wise matching are optimized in a collaborative scheme with mutually reinforcing relationship. Extensive experiments demonstrate the superior performance of our proposed method compared with benchmark ones.

Clustering objects remains challenges in seeking an under-lying partition by exploiting multiple views. Popular clustering algorithms focus on designing various constraints to handle particular representation tasks, all of which rely on a predefined pairwise similarity (sample-to-sample). However, the pairwise similarity is notoriously vulnerable to noise or outliers contaminations, resulting in sub-optimal clustering performances. To tackle the issue, this paper proposes to enhance multi-view clustering by exploring varieties of high-order statistics within multi-view data, named by HIgh-order Similarity and essential Tensor clustering method (HIST). The HIST incorporates both high-order similarity (samples-to-samples) and high-order correlation (view-to-view) into an adaptive learning model to comprehensively exploit the inherent clustering structure. Experimental results on six real datasets show the superiority of our approach over the ten popular methods.

Accurately identifying the interactions between genomic factors and the response of cancer drugs plays important roles in drug discovery, drug repositioning and cancer treatment. A number of studies revealed that interactions between genes and drugs were 'many-genes-to-many drugs' interactions, i.e. common modules, opposed to 'one-gene-to-one-drug' interactions. Such modules fully explain the interactions between complex biological regulatory mechanisms and cancer drugs. However, strategies for effectively and robustly identifying the underlying common modules among pharmacogenomics data remain to be improved. In this paper, we aim to provide a detailed evaluation of three categories of state-of-the-art common module identification techniques from a machine learning perspective, including non-negative matrix factorization (NMF), partial least squares (PLS) and network analyses. We first evaluate the performance of six methods, namely SNMNMF, NetNMF, SNPLS, O2PLS, NSBM and HOGMMNC, using two series of simulated data sets with different noise levels and outlier ratios. Then, we conduct experiments using a real world data set of 2091 genes and 101 drugs in 392 cancer cell lines and compare the real experimental results from the aspect of biological process term enrichment, gene-drug and drug-drug interactions. Finally, we present interesting findings from our evaluation study and discuss the advantages and drawbacks of each method. Supplementary information: Supplementary file is available at Briefings in Bioinformatics online.

Multi-view clustering aims to partition objects into potential categories by utilizing cross-view information. One of the core issues is to sufficiently leverage different views to learn a latent subspace, within which the clustering task is performed. Recently, it has been shown that representing the multi-view data by a tensor and then learning a latent self-expressive tensor is effective. However, early works mainly focus on learning essential tensor representation from multi-view data and the resulted affinity matrix is considered as a byproduct or is computed by a simple average in Euclidean space, thereby destroying the intrinsic clustering structure. To that end, here we proposed a novel multi-view clustering method to directly learn a well-structured affinity matrix driven by the clustering task on Grassmann manifold. Specifically, we firstly employed a tensor learning model to unify multiple feature spaces into a latent low-rank tensor space. Then each individual view was merged on Grassmann manifold to obtain both an integrative subspace and a consensus affinity matrix, driven by clustering task. The two parts are modeled by a unified objective function and optimized jointly to mine a decomposable affinity matrix. Extensive experiments on eight real-world datasets show that our method achieves superior performances over other popular methods.

The human retinal surface resembles to a sphere while it is captured by two-dimensional (2D) planar imaging to have a stereo sequence in clinical practice. Reconstructing its three-dimensional (3D) structure from the 2D planar retinal images is crucial for analyzing the relationship between the topological morphology and clinical implication. In this regard, we propose to reconstruct the 3D retina structure from 2D stereo fundus images via dynamic registration. The fundus images from different viewpoints are first co-registrated by using multi-scale deep convolutional feature and geometric structure feature by building their transformation function. The aligned images are then mosaicked together and a 3D reconstruction is obtained by a learned weighted smoothing project the registered images onto 3D coordinates. We compare the proposed registration method with five state-of-the-art methods. Extensive experimental results demonstrate that the proposed framework achieves superior performances, even with challenging scenarios in which the tested images are severely degraded by illness, large eyeball rotation and low resolutions.

Multi-view clustering aims to partition the data into their underlying clusters via leveraging multiple views information. To exploit cross-view information, existed approaches in tensor-based subspace learning attract much attention. In order to explore essential tensor, the most recent work mainly focuses on capturing representation tensor with sparse and low-rank constraints. However, one shortcoming is that this process may suffer from instability since it did not consider retaining local structure between samples. To tackle the issue, we introduce a novel self-expressive tensor learning method considering both global and local constraints to promote the learning of representation tensor. In particular, we construct a tensor-based subspace representation that joint low-rank and graph-regularized tensor learning to a united optimization problem. The essential global structure and high-order correlations can be naturally captured through low-rank self-expressive tensor learning. Meanwhile, the local structures can be preserved by introducing graph regularized terms on representation tensor, thus bring benefits to subsequent clustering task. An effective optimization procedure for solving the proposed model is presented. We conduct extensive experiments on text, object, and gene expression datasets. The experimental results well demonstrate that the proposed method, named by TLGRL, achieves superiority over benchmark methods.