Modeling intra- and inter-pair correlation via heterogeneous high-order preserving for cross-modal retrieval

Wang, Leiquan; Sun, Weichen; Zhao, Zhicheng; Su, Fei

doi:10.1016/j.sigpro.2016.08.012

Cited by 20 publications

(10 citation statements)

References 45 publications

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“…Graph-based learning [30,31] has attracted great interests in classification tasks due to its effectiveness and flexibility to various areas. A graph describes the pairwise relationships based on the given data, where vertices are labeled and unlabeled samples and edges indicate the relationships of vertices.…”

Section: Single Graph-based Learning For Classificationmentioning

confidence: 99%

Multiview Graph Learning for Small‐ and Medium‐Sized Enterprises’ Credit Risk Assessment in Supply Chain Finance

Zhang

2021

Complexity

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In recent years, supply chain finance (SCF) is exploited to solve the financing difficulties of small- and medium-sized enterprises (SMEs). SME credit risk assessment is a critical part in the SCF system. The diffusion of SME credit risk may cause serious consequences, leading the whole supply chain finance system unstable and insecure. Compared with traditional credit risk assessment models, the supply chain relationship, credit condition of SME, and core enterprises should all be considered to rate SME credit risk in SCF. Traditional methods mix all indicators from different index systems. They cannot give a quantitative result on how these index systems work. Furthermore, traditional credit risk assessment models are heavily dependent on the number of annotated SME data. However, it is implausible to accumulate enough credit risky SMEs in advance. In this paper, we propose an adaptive heterogenous multiview graph learning method to tackle the small sample size problem for SMEs’ credit risk forecasting. Three graphs are constructed by using indicators from supply chain operation, SME financial indicator, and nonfinancial indicator individually. All the graphs are integrated in an adaptive manner, providing a quantitative explanation on how the three parts cooperate. The experimental analysis shows that the proposed method has good performance for determining whether SME is risky or nonrisky in SCF. From the perspective of SCF, SME financing ability is still the main factor to determine the credit risk of SME.

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Section: Single Graph-based Learning For Classificationmentioning

confidence: 99%

Multiview Graph Learning for Small‐ and Medium‐Sized Enterprises’ Credit Risk Assessment in Supply Chain Finance

Zhang

2021

Complexity

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“…Users need a variety of hybrid modalities of retrieval, so that retrieval methods based on single modal [1]- [3] data can no longer meet people's needs. The crossmodal retrieval [4]- [6] technology has emerged in a historic moment and, owing to its great significance in both theoretical research and practical applications, it will gradually become the most popular research direction in the field of information retrieval.…”

Section: Introductionmentioning

confidence: 99%

Semantic Consistency Cross-Modal Retrieval With Semi-Supervised Graph Regularization

Zhang

2020

IEEE Access

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Most of the existing cross-modal retrieval methods make use of labeled data to learn projection matrices for different modal data. These methods usually learn the original semantic space to bridge the heterogeneous gap, ignoring the rich semantic information contained in unlabeled data. Accordingly, a semantic consistency cross-modal retrieval with semi-supervised graph regularization (SCCMR) algorithm is proposed, which integrates the prediction of labels and the optimization of projection matrices into a unified framework to ensure that the solution obtained is globally optimal. At the same time, the method uses graph embedding to consider the nearest neighbors in the potential subspace of paired images and texts as well as images and texts with the same semantics. l 21-norm constraint is applied to the projection matrices to select the discriminative features for different modal data. The results show that our method outperforms several advanced methods on four commonly used cross-modal retrieval datasets.

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“…However, it is not easy because it requires detailed knowledge of the content of each modality and the correspondence between them [6]. A variety of tools are used to construct the shared space, such as canonical correlation analysis (CCA) [1,[7][8][9][10], topic model [11][12][13], and hashing [14][15][16][17][18]. Among these methods, the deep neural network (DNN) has become the most popular one because of its strong learning ability [6,[19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…Although it may not be explicitly announced, two types of relationships are essential considerations when constructing the shared representation space: the intermodal relation and the intramodal relation [5,10]. ey play critical roles in preserving the cross-modal similarity and the single-modal similarity, respectively [5,10,25]. Also, separate representation learning and shared representation learning in some existing works are preserving these two relationships [26,27].…”

Section: Introductionmentioning

confidence: 99%

“…It should be noticed that the information to maintain these two relationships is different: the correspondence between cross-modal samples is essential to preserve intermodal relations, while the similarity relation between single-modal samples is indispensable to preserve intramodal relations [10]. Most of the existing methods, such as [5,25,[28][29][30], only use coupled cross-modal samples to preserve intermodal relations and intramodal relations; however, uncoupled single-modal samples are discarded.…”

Section: Introductionmentioning

confidence: 99%

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Abstraction and Association: Cross-Modal Retrieval Based on Consistency between Semantic Structures

Zheng

Ren

Liu

et al. 2020

Mathematical Problems in Engineering

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Cross-modal retrieval aims to find relevant data of different modalities, such as images and text. In order to bridge the modality gap, most existing methods require a lot of coupled sample pairs as training data. To reduce the demands for training data, we propose a cross-modal retrieval framework that utilizes both coupled and uncoupled samples. The framework consists of two parts: Abstraction that aims to provide high-level single-modal representations with uncoupled samples; then, Association links different modalities through a few coupled training samples. Moreover, under this framework, we implement a cross-modal retrieval method based on the consistency between the semantic structure of multiple modalities. First, both images and text are represented with the semantic structure-based representation, which represents each sample as its similarity from the reference points that are generated from single-modal clustering. Then, the reference points of different modalities are aligned through an active learning strategy. Finally, the cross-modal similarity can be measured with the consistency between the semantic structures. The experiment results demonstrate that given proper abstraction of single-modal data, the relationship between different modalities can be simplified, and even limited coupled cross-modal training data are sufficient for satisfactory retrieval accuracy.

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Modeling intra- and inter-pair correlation via heterogeneous high-order preserving for cross-modal retrieval

Cited by 20 publications

References 45 publications

Multiview Graph Learning for Small‐ and Medium‐Sized Enterprises’ Credit Risk Assessment in Supply Chain Finance

Multiview Graph Learning for Small‐ and Medium‐Sized Enterprises’ Credit Risk Assessment in Supply Chain Finance

Semantic Consistency Cross-Modal Retrieval With Semi-Supervised Graph Regularization

Abstraction and Association: Cross-Modal Retrieval Based on Consistency between Semantic Structures

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