Spectral clustering (SC) has been proven to be effective in various applications. However, the learning scheme of SC is suboptimal in that it learns the cluster indicator from a fixed graph structure, which usually requires a rounding procedure to further partition the data. Also, the obtained cluster number cannot reflect the ground truth number of connected components in the graph. To alleviate these drawbacks, we propose a rank-constrained SC with flexible embedding framework. Specifically, an adaptive probabilistic neighborhood learning process is employed to recover the block-diagonal affinity matrix of an ideal graph. Meanwhile, a flexible embedding scheme is learned to unravel the intrinsic cluster structure in low-dimensional subspace, where the irrelevant information and noise in high-dimensional data have been effectively suppressed. The proposed method is superior to previous SC methods in that: 1) the block-diagonal affinity matrix learned simultaneously with the adaptive graph construction process, more explicitly induces the cluster membership without further discretization; 2) the number of clusters is guaranteed to converge to the ground truth via a rank constraint on the Laplacian matrix; and 3) the mismatch between the embedded feature and the projected feature allows more freedom for finding the proper cluster structure in the low-dimensional subspace as well as learning the corresponding projection matrix. Experimental results on both synthetic and real-world data sets demonstrate the promising performance of the proposed algorithm.

Hashing could significantly accelerate large-scale image search by transforming the high-dimensional features into binary Hamming space, where efficient similarity search can be achieved with very fast Hamming distance computation and extremely low storage cost. As an important branch of hashing methods, multi-view hashing takes advantages of multiple features from different views for binary hash learning. However, existing multi-view hashing methods are either based on shallow models which fail to fully capture the intrinsic correlations of heterogeneous views, or unsupervised deep models which suffer from insufficient semantics and cannot effectively exploit the complementarity of view features. In this paper, we propose a novel Deep Collaborative Multi-view Hashing (DCMVH) method to deeply fuse multi-view features and learn multi-view hash codes collaboratively under a deep architecture. DCMVH is a new deep multi-view hash learning framework. It mainly consists of 1) multiple view-specific networks to extract hidden representations of different views, and 2) a fusion network to learn multi-view fused hash code. DCMVH associates different layers with instance-wise and pair-wise semantic labels respectively. In this way, the discriminative capability of representation layers can be progressively enhanced and meanwhile the complementarity of different view features can be exploited effectively. Finally, we develop a fast discrete hash optimization method based on augmented Lagrangian multiplier to efficiently solve the binary hash codes. Experiments on public multi-view image search datasets demonstrate our approach achieves substantial performance improvement over state-of-the-art methods.

Inspired by the powerful representation capability of deep neural networks, deep cross-modal hashing methods have recently drawn much attention and various deep cross-modal hashing methods have been developed. However, two key problems have not been solved well yet: 1) With advanced neural network models, how to seek the multi-modal alignment space which can effectively model the intrinsic multi-modal correlations and reduce the heterogeneous modality gaps. 2) How to effectively and efficiently preserve the modelled multi-modal semantic correlations into the binary hash codes under the deep learning paradigm. In this paper, we propose a Hierarchical Message Aggregation Hashing (HMAH) method within an efficient teacher-student learning framework. Specifically, on the teacher end, we develop hierarchical message aggregation networks to construct a multi-modal complementary space by aggregating the semantic messages hierarchically across different modalities, which can better align the heterogeneous modalities and model the fine-grained multi-modal correlations. On the student end, we train a couple of student modules that learn hash functions to support cross-modal retrieval. We design a cross-modal correlation knowledge distillation strategy which seamlessly transfers the modelled fine-grained multi-modal semantic correlations from the teacher to the lightweight student modules. With the fine-grained knowledge supervision from teacher module, the semantic representation capability of hash functions can be enhanced. In addition, the whole learning framework avoids the time-consuming finetuning on the pre-trained deep models as existing methods and it is computationally efficient. Experimental results demonstrate the significant performance improvement of the proposed method on both retrieval accuracy and efficiency, compared with the state-of-the-art deep cross-modal hashing methods. The source codes of our method are available at: https://github.com/FutureTwT/HMAH.

Long non-coding RNAs (lncRNAs) can regulate gene expression directly or indirectly through interacting with microRNAs (miRNAs). However, the role of differentially expressed mRNAs, lncRNAs and miRNAs, and especially their related competitive endogenous RNAs (ceRNA) network in head and neck squamous cell carcinoma (HNSCC), is not fully comprehended. In this paper, the lncRNA, miRNA, and mRNA expression profiles of 546 HNSCC patients, including 502 tumor and 44 adjacent non-tumor tissues, from The Cancer Genome Atlas (TCGA) were analyzed. 82 miRNAs, 1197 mRNAs and 1041 lncRNAs were found to be differentially expressed in HNSCC samples (fold change ≥ 2; P < 0.01). Further bioinformatics analysis was performed to construct a lncRNA-miRNA-mRNA ceRNA network of HNSCC, which includes 8 miRNAs, 71 lncRNAs and 16 mRNAs. Through survival analysis based on the expression profiles of RNAs in the ceRNA network, we detected 1 mRNA, 1 miRNA and 13 lncRNA to have a significant impact on the overall survival of HNSCC patients (P < 0.05). Finally, some lncRNAs, which are more important for survival, were also predicted. Our research provides data to further understand the molecular mechanisms implicated in HNSCC.

Recent studies show that hashing technology can achieve efficient similarity searching and many works have been done on supervised deep hash learning. However, under unsupervised scenarios, there are several issues to be solved when learning hashing codes based on visual features for image retrieval and clustering. In this paper, we propose a simple but effective Unsupervised Deep K-means Hashing (UDKH) method to simultaneously alleviate the problems of image retrieval and clustering within a single learning framework. UDKH progressively improves the quality of cluster labels and binary hash codes by minimizing pair-wise supervision loss and optimizing the binary K-means to generate discriminative hash codes under the supervision of the learned cluster labels for effective image retrieval. Since the learned hash codes are discriminative, UDKH also improves the image clustering accuracy. Experiments on test datasets demonstrate its effectiveness for image retrieval and clustering.