Affinity Matrix Research Articles

A credit scoring model based on the Myers–Briggs type indicator in online peer-to-peer lending

Although psychometric features have been considered for alternative credit scoring, they have not yet been applied to peer-to-peer (P2P) lending because such information is not available on platforms. This study proposed an alternative credit scoring model for P2P lending by extracting typical personality types inferred from the borrowers’ job category. We projected a virtual space of borrowers by using the affinity matrix based on the Myers–Briggs type indicator (MBTI) that fits each job category. Applying the distance in this space to Lending Club data, we used locally weighted logistic regression to vary the coefficients of the variables, which affect loan repayments, with each MBTI type for predicting the default probability. We found that each MBTI type’s credit scoring model has different significant variables. This study provides insights into breakthroughs in developing alternative credit scoring for P2P lending.

Protective effect of ophiopogonin D against isoproterenol-induced cardiomyocyte injury and targets

This study aims to unveil the effect of ophiopogonin D(OPD) on isoproterenol(ISO)-induced apoptosis of rat cardiomyocytes and the possible targets, which is expected to provide clues for further research on the myocardial protection of ophiopogonins. Cell count kit-8(CCK-8) assay was used to detect viability of cells treated with OPD and ISO, Western blot to examine the effect of OPD and ISO on the expression of endoplasmic reticulum stress-related Bip, Bax, Perk, ATF4, caspase-12, and CHOP, flow cytometry to determine cell apoptosis rate, and Hoechst 33258 and Tunel staining to observe cell apoptosis and morphological changes. In addition, the probe for calcium ion-specific detection was employed to investigate calcium ion release from the endoplasmic reticulum, and OPD-bond epoxy-activated agarose solid-phase microspheres were prepared and used as affinity matrix to capture OPD-binding target proteins in H9 c2 cell lysate. For the target proteins of OPD identified by high-resolution mass spectrometry, the related signal pathways were enriched and the potential targets of OPD against cardiomyocyte injury were discussed. The experimental result showed that 10 μmol·L~(-1) ISO can significantly induce the expression of endoplasmic reticulum stress-related proteins and promote cell apoptosis. Different concentration of OPD can prevent the damage of myocardial cells caused by ISO. According to mass spectrometry results, 19 proteins, including Fam129 a and Pdia6, were involved in multiple signaling pathways such as the unfolded protein reaction bound by the ERN1 sensor, tricarboxylic acid cycle, and Nrf2 signal transduction pathway. The above results indicate that OPD protects cardiomyocytes by regulating multiple signaling pathways of target proteins and affecting cell cycle progression.

Component‐based nearest neighbour subspace clustering

In this paper, the problem of clustering data points that lie near or on a union of independent low-dimensional subspaces is addressed. To this end, the popular spectral clustering-based algorithms usually follow a two-stage strategy that initially builds an affinity matrix and then applies spectral clustering. However, an inappropriate affinity matrix that does not sufficiently connect data points lying on the same subspace will easily lead to the issue of over-segmentation. To alleviate this issue, building the affinity matrix based on subspace hypotheses generated by an iterative sampling operation according to the Random Cluster Model under the framework of energy minimisation is proposed. Specifically, each hypothesis is generated from a large number of data points by sampling a component in a K-nearest neighbour graph. Extensive experiments on synthetic data and real-world datasets show that the proposed method can improve the connectivity of the affinity matrix and provide competitive results against state-of-the-art methods.

Three-dimensional affinity learning based multi-branch ensemble network for breast tumor segmentation in MRI

Accurate and automatic breast tumor segmentation based on dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) plays an important role in breast cancer analysis. However, the background parenchymal enhancement and large variations in tumor size, shape or appearance make the task very challenging, and also the segmentation performance is still not satisfactory, especially for non-mass enhancement (NME) and small size tumors (≤2 cm). To address these challenges, we propose a novel 3D affinity learning based multi-branch ensemble network for accurate breast tumor segmentation. Specifically, two different types of subnetworks are built to form a multi-branch network. The two subnetworks are equipped with effective operation components, i.e., residual connection and channel-wise attention or making use of dense connectivity patterns, which can process the input images in parallel. Second, we propose an end-to-end trainable 3D affinity learning based refinement module by calculating the similarities between features of voxels, which is useful in discovering more pixels belonging to breast tumors. Third, two local affinity matrices are constructed by 3D affinity learning, which are used to refine the segmentation outputs of two subnetworks, respectively. Finally, a novel ensemble module is proposed to combine the information derived from the subnetworks, which can hierarchically merge the local and global affinity matrices derived from subnetworks. A large-scale breast DCE-MR images dataset with 420 subjects are built for evaluation, and comprehensive experiments have been conducted to demonstrate that our proposed method achieves superior performance over state-of-the-art medical image segmentation methods.

AdapGL: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks

With well-defined graphs, graph convolution based spatiotemporal neural networks for traffic prediction have achieved great performance in numerous tasks. Compared to other methods, the networks can exploit the latent spatial dependencies between nodes according to the adjacency relationship. However, as the topological structure of the real road network tends to be intricate, it is difficult to accurately quantify the correlations between nodes in advance. In this paper, we propose a graph convolutional network based adaptive graph learning algorithm (AdapGL) to acquire the complex dependencies. First, by developing a novel graph learning module, more possible correlations between nodes can be adaptively captured during training. Second, inspired by the expectation maximization (EM) algorithm, the parameters of the prediction network module and the graph learning module are optimized by alternate training. An elaborate loss function is leveraged for graph learning to ensure the sparsity of the generated affinity matrix. In this way, the expectation maximization of one part can be realized under the condition that the other part is the best estimate. Finally, the graph structure is updated by a weighted sum approach. The proposed algorithm can be applied to most graph convolution based networks for traffic forecast. Experimental results demonstrated that our method can not only further improve the accuracy of traffic prediction, but also effectively exploit the hidden correlations of the nodes. The source code is available at https://github.com/goaheand/AdapGL-pytorch.

MDICC: novel method for multi-omics data integration and cancer subtype identification.

Each type of cancer usually has several subtypes with distinct clinical implications, and therefore the discovery of cancer subtypes is an important and urgent task in disease diagnosis and therapy. Using single-omics data to predict cancer subtypes is difficult because genomes are dysregulated and complicated by multiple molecular mechanisms, and therefore linking cancer genomes to cancer phenotypes is not an easy task. Using multi-omics data to effectively predict cancer subtypes is an area of much interest; however, integrating multi-omics data is challenging. Here, we propose a novel method of multi-omics data integration for clustering to identify cancer subtypes (MDICC) that integrates new affinity matrix and network fusion methods. Our experimental results show the effectiveness and generalization of the proposed MDICC model in identifying cancer subtypes, and its performance was better than those of currently available state-of-the-art clustering methods. Furthermore, the survival analysis demonstrates that MDICC delivered comparable or even better results than many typical integrative methods.

Crumbs3 is expressed in oral squamous cell carcinomas and promotes cell migration and proliferation by affecting RhoA activity.

Despite the recent progression of treatments, the 5-year survival rate of patients with oral squamous cell carcinoma (OSCC) is still poor. One of the most critical factors affecting prognosis is tumor metastasis. Developing novel molecular targeted therapies by analyzing the molecular pathway of OSCC metastasis is an urgent issue. The present study aimed to characterize the expression and function of crumbs3 (Crb3) in OSCC cell migration. Immunohistochemistry and immunoblotting revealed that Crb3 was expressed in tissues from patients with OSCC and OSCC cell lines. The motility of OSCC cell lines was decreased by knockdown of Crb3 without affecting proliferation. However, Crb3-knockout (KO) clones exhibited decreases in both cell migration and proliferation. The expression of epithelial-mesenchymal transition markers was not altered in Crb3-KO clones compared with parent cells. A xenograft mouse model of lung metastasis revealed that the metastatic potential of Crb3-KO clones was reduced. As seen with Crb3-KO clones, the motility of OSCC cells was decreased by treatment with inhibitors of RhoA activation. Serum-induced activation of RhoA in OSCC cells was evaluated by comparing the amount of GTP-bound RhoA using affinity matrices, revealing that RhoA activation was decreased in Crb3-KO clones. To the best of our knowledge, the present study was the first to demonstrate that Crb3 was expressed in squamous cell carcinoma tissues and promoted cell migration and proliferation, which was associated with RhoA activation in OSCC cells.

Weighted p-norm distance t kernel SVM classification algorithm based on improved polarization

The kernel function in SVM enables linear segmentation in a feature space for a large number of linear inseparable data. The kernel function that is selected directly affects the classification performance of SVM. To improve the applicability and classification prediction effect of SVM in different areas, in this paper, we propose a weighted p-norm distance t kernel SVM classification algorithm based on improved polarization. A t-class kernel function is constructed according to the t distribution probability density function, and its theoretical proof is presented. To find a suitable mapping space, the t-class kernel function is extended to the p-norm distance kernel. The training samples are obtained by stratified sampling, and the affinity matrix is redefined. The improved local kernel polarization is established to obtain the optimal kernel weights and kernel parameters so that different kernel functions are weighted combinations. The cumulative optimal performance rate is constructed to evaluate the overall classification performance of different kernel SVM algorithms, and the significant effects of different p-norms on the classification performance of SVM are verified by 10 times fivefold cross-validation statistical comparison tests. In most cases, the results using 6 real datasets show that compared with the traditional kernel function, the proposed weighted p-norm distance t kernel can improve the classification prediction performance of SVM.

Spectral clustering of single cells using Siamese nerual network combined with improved affinity matrix.

Limitations of bulk sequencing techniques on cell heterogeneity and diversity analysis have been pushed with the development of single-cell RNA-sequencing (scRNA-seq). To detect clusters of cells is a key step in the analysis of scRNA-seq. However, the high-dimensionality of scRNA-seq data and the imbalances in the number of different subcellular types are ubiquitous in real scRNA-seq data sets, which poses a huge challenge to the single-cell-type detection.We propose a meta-learning-based model, SiaClust, which is the combination of Siamese Convolutional Neural Network (CNN) and improved spectral clustering, to achieve scRNA-seq cell type detection. To be specific, with the help of the constrained Sigmoid kernel, the raw high-dimensionality data is mapped to a low-dimensional space, and the Siamese CNN learns the differences between the cell types in the low-dimensional feature space. The similarity matrix learned by Siamese CNN is used in combination with improved spectral clustering and t-distribution Stochastic Neighbor Embedding (t-SNE) for visualization. SiaClust highlights the differences between cell types by comparing the similarity of the samples, whereas blurring the differences within the cell types is better in processing high-dimensional and imbalanced data. SiaClust significantly improves clustering accuracy by using data generated by nine different species and tissues through different scNA-seq protocols for extensive evaluation, as well as analogies to state-of-the-art single-cell clustering models. More importantly, SiaClust accurately locates the exact site of dropout gene, and is more flexible with data size and cell type.

Double structure scaled simplex representation for multi-view subspace clustering

In the era of big data, there are an increasing number of multisource information data, and multi-view clustering (MVC) algorithms have developed rapidly. However, the affinity matrix learned by most MVC methods is not clean and precise enough and cannot describe the latent structure of multi-view data accurately, which results in poor clustering performance. In this paper, we propose a novel Double Structure Scaled Simplex Representation (DSSSR) method for MVC. Initially, we concatenate the multi-view data into a joint representation. Then, we use the scaled simplex representation (SSR) method on the concatenated data to obtain the affinity matrix. However, the affinity matrix is not clean and precise. Therefore, we use the SSR method again on the obtained affinity matrix to obtain a more accurate and clean affinity matrix. Furthermore, the two-step SSR is integrated into a unified optimization framework, a clean and accurate affinity matrix can be obtained, and the sum of each column vector of the affinity matrix is constrained to be nonnegative and equal to s (0<s<1), which can be adjusted to obtain the best clustering performance. Finally, an efficient optimization algorithm based on the augmented Lagrangian method (ALM) for solving the objective function is also designed. The experimental results on some datasets show that this algorithm has better clustering performance than some state-of-the-art algorithms.

Spectral clustering with anchor graph based on set-to-set distances for large-scale hyperspectral images

ABSTRACT Since labelled samples of hyperspectral images (HSIs) may be unavailable in practical remote sensing applications, large-scale HSI clustering is very important. Due to the huge amount of data brought by the rich spectral and spatial information in large-scale HSIs, HSI clustering is still a challenging task. Among the methods designed for large-scale HSIs clustering, the anchor graph-based methods simultaneously inherit the merits of graph-based clustering and reduce the computational complexity by introducing anchor samples to graph construction. However, the affinity matrix computed by inaccurate distances between anchor samples and other HSI samples can hardly obtain satisfactory clustering performance. To solve this problem, we propose a novel approach for large-scale HSI clustering, namely, spectral clustering with anchor graph based on set-to-set distances (SCAG-SSD) derived from local covariance matrix representation (LCMR). First, superpixels and LCMR features of HSI are obtained via the entropy rate superpixel algorithm and maximum noise fraction, respectively. Second, pure and anomalous samples of each superpixel are distinguished via the distances of LCMR features, and then anchor samples are selected via statistics of the distances between pure samples in each superpixel. In this way, selected anchor samples are representative enough to link all the HSI samples. Third, pure samples in each superpixel, anomalous and anchor samples with their corresponding nearest neighbouring samples are all regarded as different sets. The set-to-set distance is achieved by weighting the LCMR-based distances between samples of two sets. Finally, fast anchor graph clustering is conducted based on set-to-set distances to obtain final clustering maps. Extensive experiments conducted on three publicly available benchmark HSIs demonstrate that the proposed method achieves state-of-the-art clustering accuracy with comparable efficiency.

STPNet: A Spatial-Temporal Propagation Network for Background Subtraction

In background subtraction tasks, spatial and temporal contexts are beneficial in detecting moving objects. The methods based on Deep Neural Networks in this task has explored different topologies, which are composed of the conventional operations of convolutional neural networks, such as Convolutional Long-short Term Memory layer (ConvLSTM), 2D convolutional layer, or 3D convolutional layer, to capture these contexts. In this work, we propose a new background subtraction algorithm named spatial–temporal propagation network. An end-to-end network with novel layers, whose process of operation is equivalent to that the feature maps multiply with affinity matrices, is proposed to capture the spatial–temporal correlation in video sequences and aggregate the deep features from the consecutive frames. Experimental results on CDnet-2014 and LASIESTA datasets show that this novel layer provides an alternative way for our network to aggregate multiscale spatial–temporal features. Meanwhile, the proposed network achieves state-of-the-art performance and is generalizable to unseen videos.

Maximum Entropy Subspace Clustering Network

Deep subspace clustering networks have attracted much attention in subspace clustering, in which an auto-encoder non-linearly maps the input data into a latent space, and a fully connected layer named self-expressiveness module is introduced to learn the affinity matrix via a typical regularization term (e.g., sparse or low-rank). However, the adopted regularization terms ignore the connectivity within each subspace, limiting their clustering performance. In addition, the adopted framework suffers from the coupling issue between the auto-encoder module and the self-expressiveness module, making the network training non-trivial. To tackle these two issues, we propose a novel deep subspace clustering method named Maximum Entropy Subspace Clustering Network (MESC-Net). Specifically, MESC-Net maximizes the entropy of the affinity matrix to promote the connectivity within each subspace, in which its elements corresponding to the same subspace are uniformly and densely distributed. Meanwhile, we design a novel framework to explicitly decouple the auto-encoder module and the self-expressiveness module. Besides, we also theoretically prove that the learned affinity matrix satisfies the block-diagonal property under the assumption of independent subspaces. Extensive quantitative and qualitative results on commonly used benchmark datasets validate MESC-Net significantly outperforms state-of-the-art methods. The code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ZhihaoPENG-CityU/MESC</uri> .

A Derivative-Free Affine Scaling LP Algorithm Based on Probabilistic Models for Linear Inequality Constrained Optimization

In this paper, a derivative-free affine scaling linear programming algorithm based on probabilistic models is considered for solving linear inequality constrainted optimization problems. The proposed algorithm is designed to build probabilistic linear polynomial interpolation models using only n + 1 interpolation points for the objective function and reduce the computation cost for building interpolation function. We build the affine scaling linear programming methods which use probabilistic or random models and affine matrix within a classical linear programming framework. The backtracking line search technique can guarantee monotone descent of the objective function, and by using this technique, the new iterative points are located within the feasible region. Under some reasonable conditions, the global and local fast convergence of the algorithm is shown, and the results of numerical experiments are reported to show the effectiveness of the proposed algorithm.

Clean and robust affinity matrix learning for multi-view clustering

Clean and robust affinity matrix learning for multi-view clustering

Prediction of Synergistic Antibiotic Combinations by Graph Learning.

Antibiotic resistance is a major public health concern. Antibiotic combinations, offering better efficacy at lower doses, are a useful way to handle this problem. However, it is difficult for us to find effective antibiotic combinations in the vast chemical space. Herein, we propose a graph learning framework to predict synergistic antibiotic combinations. In this model, a network proximity method combined with network propagation was used to quantify the relationships of drug pairs, and we found that synergistic antibiotic combinations tend to have smaller network proximity. Therefore, network proximity can be used for building an affinity matrix. Subsequently, the affinity matrix was fed into a graph regularization model to predict potential synergistic antibiotic combinations. Compared with existing methods, our model shows a better performance in the prediction of synergistic antibiotic combinations and interpretability.

Mining Cross-Domain Structure Affinity for Refined Building Segmentation in Weakly Supervised Constraints

Building segmentation for remote sensing images usually requires pixel-level labels which is difficult to collect when the images are in low resolution and quality. Recently, weakly supervised semantic segmentation methods have achieved promising performance, which only rely on image-level labels for each image. However, buildings in remote sensing images tend to present regular structures. The lack of supervision information may result in the ambiguous boundaries. In this paper, we propose a new weakly supervised network for refined building segmentation by mining the cross-domain structure affinity (CDSA) from multi-source remote sensing images. CDSA integrates the ideas of weak supervision and domain adaptation, where a pixel-level labeled source domain and an image-level labeled target domain are required. The target of CDSA is to learn a powerful segmentation network on the target domain with the guidance of source domain data. CDSA mainly consists of two branches, the structure affinity module (SAM) and the spatial structure adaptation (SSA). In brief, SAM is developed to learn the structure affinity of the buildings from source domain, and SSA infuses the structure affinity to the target domain via a domain adaptation approach. Moreover, we design an end-to-end network structure to simultaneously optimize the SAM and SSA. In this case, SAM can receive pseudosupervised information from SSA, and in turn provide a more accurate affinity matrix for SSA. In the experiments, our model can achieve an IoU score at 57.87% and 79.57% for the WHU and Vaihingen data sets. We compare CDSA with several state-of-the-art weakly supervised and domain adaptation methods, and the results indicate that our method presents advantages on two public data sets.

Joint learning affinity matrix and representation matrix for robust low-rank multi-kernel clustering

Joint learning affinity matrix and representation matrix for robust low-rank multi-kernel clustering

Instance segmentation of biological images using graph convolutional network

Instance segmentation in biological images is an important task in the field of biological images and biomedical analysis. Different from the instance segmentation of natural image scenes, this task is still challenging because there are a large number of overlapping objects with similar appearance as well as great variability in shape, size and texture in the foreground and background. In this paper, we propose a novel method for segmentation of graph-guided instances of biological images, which successfully addresses these peculiarities. Our method predicts the embedding at each pixel and uses clustering to recover instances during testing. Specifically, we design the Graph-guided Feature Fusion Module in response to overlapping instances. Our Graph-guided Feature Fusion Module combines fine deep features and coarse shallow features to learn the affinity matrix, and then uses graph convolutional network to guide the network to learn object-level local features. Next, we devise the Gated Spatial Attention Module to effectively learn key spatial information by introducing a gating mechanism. Furthermore, we give the Cluster Distance Loss that can effectively distinguish foreground objects from similar backgrounds. The effectiveness of our proposed method has been verified on various biological and biomedical datasets. The experimental results show that our method is superior to previous embedding-based instance segmentation methods. The SBD metric for our method reached 90.8% on the plant phenotype dataset (CVPPP), 72.5% on the cell nucleus dataset (DSB2018), and 81.8% on the C.elegans dataset, all achieving state-of-the-art performance.

Learning Static-Adaptive Graphs for RGB-T Image Saliency Detection

Many works have been proposed on image saliency detection to handle challenging issues including low illumination, cluttered background, low contrast, and so on. Although good performance has been achieved by these algorithms, detection results are still poor based on RGB modality. Inspired by the recent progress of multi-modality fusion, we propose a novel RGB-thermal saliency detection algorithm through learning static-adaptive graphs. Specifically, we first extract superpixels from the two modalities and calculate their affinity matrix. Then, we learn the affinity matrix dynamically and construct a static-adaptive graph. Finally, the saliency maps can be obtained by a two-stage ranking algorithm. Our method is evaluated on RGBT-Saliency Dataset with eleven kinds of challenging subsets. Experimental results show that the proposed method has better generalization performance. The complementary benefits of RGB and thermal images and the more robust feature expression of learning static-adaptive graphs create an effective way to improve the detection effectiveness of image saliency in complex scenes.