Joint Graph Regularization Research Articles

Sparse sampling photoacoustic reconstruction with a graph regularization group sparse dictionary

Photoacoustic tomography (PAT) has emerged as a promising biomedical imaging technique. The combination of optical contrast and ultrasound spatial resolution in photoacoustic tomography overcomes the limitations of optical scattering, enabling clear imaging of tissue structures. However, achieving high-resolution photoacoustic images typically requires a large number of sensor detection elements for sufficient angular coverage. This demand for extensive data acquisition and processing raises concerns about efficiency and system complexity. While sparse sampling strategies can improve efficiency, preserving detailed structural information becomes challenging with a minimal number of detectors. To address the challenges of sparse sampling, compressed sensing (CS) techniques have been successfully applied for image reconstructions in 2D and 3D photoacoustic embodiments. In this context, we propose a joint graph regularization group sparse dictionary and total variational regularization (GRGS-TV) algorithm based on our previous work of a group sparse dictionary. It preserves structured information and geometric relationships among dictionary atoms. Moreover, TV regularization effectively preserves edge structures while exhibiting a certain degree of robustness and flexibility. Numerical simulations and in vivo experiments on mice validate the effectiveness of this method in improving photoacoustic image quality and suppressing artifacts. Comparative evaluations against other algorithms show enhanced performance in terms of image reconstruction evaluation indices. This innovative approach holds promise for advancing photoacoustic imaging in biomedical research and clinical diagnostics.

Data representation using robust nonnegative matrix factorization for edge computing.

As a popular data representation technique, Nonnegative matrix factorization (NMF) has been widely applied in edge computing, information retrieval and pattern recognition. Although it can learn parts-based data representations, existing NMF-based algorithms fail to integrate local and global structures of data to steer matrix factorization. Meanwhile, semi-supervised ones ignore the important role of instances from different classes in learning the representation. To solve such an issue, we propose a novel semi-supervised NMF approach via joint graph regularization and constraint propagation for edge computing, called robust constrained nonnegative matrix factorization (RCNMF), which learns robust discriminative representations by leveraging the power of both L2, 1-norm NMF and constraint propagation. Specifically, RCNMF explicitly exploits global and local structures of data to make latent representations of instances involved by the same class closer and those of instances involved by different classes farther. Furthermore, RCNMF introduces the L2, 1-norm cost function for addressing the problems of noise and outliers. Moreover, L2, 1-norm constraints on the factorial matrix are used to ensure the new representation sparse in rows. Finally, we exploit an optimization algorithm to solve the proposed framework. The convergence of such an optimization algorithm has been proven theoretically and empirically. Empirical experiments show that the proposed RCNMF is superior to other state-of-the-art algorithms.

Adaptive multi-source multi-view latent feature learning for inferring potential disease-associated miRNAs.

Accumulating evidence has shown that microRNAs (miRNAs) play crucial roles in different biological processes, and their mutations and dysregulations have been proved to contribute to tumorigenesis. In silico identification of disease-associated miRNAs is a cost-effective strategy to discover those most promising biomarkers for disease diagnosis and treatment. The increasing available omics data sources provide unprecedented opportunities to decipher the underlying relationships between miRNAs and diseases by computational models. However, most existing methods are biased towards a single representation of miRNAs or diseases and are also not capable of discovering unobserved associations for new miRNAs or diseases without association information. In this study, we present a novel computational method with adaptive multi-source multi-view latent feature learning (M2LFL) to infer potential disease-associated miRNAs. First, we adopt multiple data sources to obtain similarity profiles and capture different latent features according to the geometric characteristic of miRNA and disease spaces. Then, the multi-modal latent features are projected to a common subspace to discover unobserved miRNA-disease associations in both miRNA and disease views, and an adaptive joint graph regularization term is developed to preserve the intrinsic manifold structures of multiple similarity profiles. Meanwhile, the Lp,q-norms are imposed into the projection matrices to ensure the sparsity and improve interpretability. The experimental results confirm the superior performance of our proposed method in screening reliable candidate disease miRNAs, which suggests that M2LFL could be an efficient tool to discover diagnostic biomarkers for guiding laborious clinical trials.

Joint Graph Regularization in a Homogeneous Subspace for Cross-Media Retrieval

The heterogeneity of multimodal data is the main challenge in cross-media retrieval; many methods have already been developed to address the problem. At present, subspace learning is one of the mainstream approaches for cross-media retrieval; its aim is to learn a latent shared subspace so that similarities within cross-modal data can be measured in this subspace. However, most existing subspace learning algorithms only focus on supervised information, using labeled data for training to obtain one pair of mapping matrices. In this paper, we propose joint graph regularization based on semi-supervised learning cross-media retrieval (JGRHS), which makes full use of labeled and unlabeled data. We jointly considered correlation analysis and semantic information when learning projection matrices to maintain the closeness of pairwise data and semantic consistency; graph regularization is used to make learned transformation consistent with similarity constraints in both modalities. In addition, the retrieval results on three datasets indicate that the proposed method achieves good efficiency in theoretical research and practical applications.

Efficient cross-modal retrieval via flexible supervised collective matrix factorization hashing

Cross-modal retrieval has recently drawn much attention in multimedia analysis, and it is still a challenging topic mainly attributes to its heterogeneous nature. In this paper, we propose a flexible supervised collective matrix factorization hashing (FS-CMFH) to efficient cross-modal retrieval. First, we exploit a flexible collective matrix factorization framework to jointly learn the individual latent space of similar semantic with respected to each modality. Meanwhile, the label consistency across different modalities is simultaneously exploited to preserve both intra-modal and inter-modal semantics within these similar latent semantic spaces. Accordingly, these two ingredients are formulated as a joint graph regularization term in an overall objective function, through which the similar hash codes of different modalities in an instance can be discriminatively obtained to flexibly characterize such instance. As a result, these derived hash codes incorporating higher discrimination power are able to improve the cross-modal searching accuracy significantly. The extensive experiments tested on three popular benchmark datasets show that the proposed approach performs favorably compared to the state-of-the-art competing approaches.

Joint graph regularization based semantic analysis for cross-media retrieval: a systematic review

The large number of heterogeneous data are rapidly increasing in the internet and most data consist of audio, video, text and images. The searching of the required data from the large database is difficult and time taking process. The single media retrieval is used to get the needed data from the large dataset and it has the drawback, it can only retrieve the single media only. If the query is given as the text and acquired result are present in text. The users demand the cross-media retrieval for their queries and it is very consistent in providing the result. This helps the users to get more information regarding to their queries. Finding the similarities between the heterogeneous data is very complex. Many research is done on the cross-media retrieval with different methods and provide the different result. The aim is to analysis the different cross-media retrieval with the joint graph regularization (JGR) to understand the various technique. The most of researches are using the parameter of MAP, precision and recall for their research.

Joint graph regularization based modality-dependent cross-media retrieval

Cross-media retrieval returns heterogeneous multimedia data of the same semantics for a query object, and the key problem for cross-media retrieval is how to deal with the correlations of heterogeneous multimedia data. Many works focus on mapping different modal data into an isomorphic space, so the similarities between different modal data can be measured. Inspired by this idea, we propose a joint graph regularization based modality-dependent cross-media retrieval approach (JGRMDCR), which takes into account the one-to-one correspondence between different modal data pairs, the inter-modality similarities and the intra-modality similarities. Meanwhile, according to the modality of the query object, this method learns different projection matrices for different retrieval tasks. Experimental results on benchmark datasets show that the proposed approach outperforms the other state-of-the-art algorithms.

Robust Classification of Information Networks by Consistent Graph Learning.

Graph regularization-based methods have achieved great success for network classification by making the label-link consistency assumption, i.e., if two nodes are linked together, they are likely to belong to the same class. However, in a real-world network, there exist links that connect nodes of different classes. These inconsistent links raise a big challenge for graph regularization and deteriorate the classification performance significantly. To address this problem, we propose a novel algorithm, namely Consistent Graph Learning, which is robust to the inconsistent links of a network. In particular, given a network and a small number of labeled nodes, we aim at learning a consistent network with more consistent and fewer inconsistent links than the original network. Since the link information of a network is naturally represented by a set of relation matrices, the learning of a consistent network is reduced to learning consistent relation matrices under some constraints. More specifically, we achieve it by joint graph regularization on the nuclear norm minimization of consistent relation matrices together with ℓ1-norm minimization on the difference matrices between the original relation matrices and the learned consistent ones subject to certain constraints. Experiments on both homogeneous and heterogeneous network datasets show that the proposed method outperforms the state-of-the-art methods.

Heterogeneous Metric Learning with Joint Graph Regularization for Cross-Media Retrieval

As the major component of big data, unstructured heterogeneous multimedia content such as text, image, audio, video and 3D increasing rapidly on the Internet. User demand a new type of cross-media retrieval where user can search results across various media by submitting query of any media. Since the query and the retrieved results can be of different media, how to learn a heterogeneous metric is the key challenge. Most existing metric learning algorithms only focus on a single media where all of the media objects share the same data representation. In this paper, we propose a joint graph regularized heterogeneous metric learning (JGRHML) algorithm, which integrates the structure of different media into a joint graph regularization. In JGRHML, different media are complementary to each other and optimizing them simultaneously can make the solution smoother for both media and further improve the accuracy of the final metric. Based on the heterogeneous metric, we further learn a high-level semantic metric through label propagation. JGRHML is effective to explore the semantic relationship hidden across different modalities. The experimental results on two datasets with up to five media types show the effectiveness of our proposed approach.