Graph-based Semi-supervised Learning Framework Research Articles

A heterogeneous graph-based semi-supervised learning framework for access control decision-making

For modern information systems, robust access control mechanisms are vital in safeguarding data integrity and ensuring the entire system’s security. This paper proposes a novel semi-supervised learning framework that leverages heterogeneous graph neural network-based embedding to encapsulate both the intricate relationships within the organizational structure and interactions between users and resources. Unlike existing methods focusing solely on individual user and resource attributes, our approach embeds organizational and operational interrelationships into the hidden layer node embeddings. These embeddings are learned from a self-supervised link prediction task based on a constructed access control heterogeneous graph via a heterogeneous graph neural network. Subsequently, the learned node embeddings, along with the original node features, serve as inputs for a supervised access control decision-making task, facilitating the construction of a machine-learning access control model. Experimental results on the open-sourced Amazon access control dataset demonstrate that our proposed framework outperforms models using original or manually extracted graph-based features from previous works. The prepossessed data and codes are available on GitHub,facilitating reproducibility and further research endeavors.

Deep semi-supervised learning for brain tumor classification

BackgroundThis paper addresses issues of brain tumor, glioma, classification from four modalities of Magnetic Resonance Image (MRI) scans (i.e., T1 weighted MRI, T1 weighted MRI with contrast-enhanced, T2 weighted MRI and FLAIR). Currently, many available glioma datasets often contain some unlabeled brain scans, and many datasets are moderate in size.MethodsWe propose to exploit deep semi-supervised learning to make full use of the unlabeled data. Deep CNN features were incorporated into a new graph-based semi-supervised learning framework for learning the labels of the unlabeled data, where a new 3D-2D consistent constraint is added to make consistent classifications for the 2D slices from the same 3D brain scan. A deep-learning classifier is then trained to classify different glioma types using both labeled and unlabeled data with estimated labels. To alleviate the overfitting caused by moderate-size datasets, synthetic MRIs generated by Generative Adversarial Networks (GANs) are added in the training of CNNs.ResultsThe proposed scheme has been tested on two glioma datasets, TCGA dataset for IDH-mutation prediction (molecular-based glioma subtype classification) and MICCAI dataset for glioma grading. Our results have shown good performance (with test accuracies 86.53% on TCGA dataset and 90.70% on MICCAI dataset).ConclusionsThe proposed scheme is effective for glioma IDH-mutation prediction and glioma grading, and its performance is comparable to the state-of-the-art.

Joint Sparse Representation and Embedding Propagation Learning: A Framework for Graph-Based Semisupervised Learning.

In this paper, we propose a novel graph-based semisupervised learning framework, called joint sparse representation and embedding propagation learning (JSREPL). The idea of JSREPL is to join EPL with sparse representation to perform label propagation. Like most of graph-based semisupervised propagation learning algorithms, JSREPL also constructs weights graph matrix from given data. Different from classical approaches which build weights graph matrix and estimate the labels of unlabeled data in sequence, JSREPL simultaneously builds weights graph matrix and estimates the labels of unlabeled data. We also propose an efficient algorithm to solve the proposed problem. The proposed method is applied to the problem of semisupervised image clustering using the ORL, Yale, PIE, and YaleB data sets. Our experiments demonstrate the effectiveness of our proposed algorithm.

Graph based semi-supervised learning via label fitting

The global smoothness and the local label fitting are two key issues for estimating the function on the graph in graph based semi-supervised learning (GSSL). The unsupervised normalized cut method can provide a more reasonable criterion for learning the global smoothness of the data than classic GSSL methods. However, the semi-supervised norm of the normalized cut, which is a NP-hard problem, has not been studied well. In this paper, a new GSSL framework is proposed by extending normalized cut to its semi-supervised norm. The NP-hard semi-supervised normalized cut problem is innovatively solved by effective algorithms. In addition, we can design more reasonable local label fitting terms than conventional GSSL methods. Other graph cut methods are also investigated to extend the proposed semi-supervised learning algorithms. Furthermore, we incorporate the nonnegative matrix factorization with the proposed learning algorithms to solve the out-of-sample problem in semi-supervised learning. Solutions obtained by the proposed algorithms are sparse, nonnegative and congruent with unit matrix. Experiment results on several real benchmark datasets indicate that the proposed algorithms achieve good results compared with state-of-art methods.

L1-norm locally linear representation regularization multi-source adaptation learning

In most supervised domain adaptation learning (DAL) tasks, one has access only to a small number of labeled examples from target domain. Therefore the success of supervised DAL in this “small sample” regime needs the effective utilization of the large amounts of unlabeled data to extract information that is useful for generalization. Toward this end, we here use the geometric intuition of manifold assumption to extend the established frameworks in existing model-based DAL methods for function learning by incorporating additional information about the target geometric structure of the marginal distribution. We would like to ensure that the solution is smooth with respect to both the ambient space and the target marginal distribution. In doing this, we propose a novel L1-norm locally linear representation regularization multi-source adaptation learning framework which exploits the geometry of the probability distribution, which has two techniques. Firstly, an L1-norm locally linear representation method is presented for robust graph construction by replacing the L2-norm reconstruction measure in LLE with L1-norm one, which is termed as L1-LLR for short. Secondly, considering the robust graph regularization, we replace traditional graph Laplacian regularization with our new L1-LLR graph Laplacian regularization and therefore construct new graph-based semi-supervised learning framework with multi-source adaptation constraint, which is coined as L1-MSAL method. Moreover, to deal with the nonlinear learning problem, we also generalize the L1-MSAL method by mapping the input data points from the input space to a high-dimensional reproducing kernel Hilbert space (RKHS) via a nonlinear mapping. Promising experimental results have been obtained on several real-world datasets such as face, visual video and object.

Local similarity learning for pairwise constraint propagation

Pairwise constraint propagation studies the problem of propagating the scarce pairwise constraints across the entire dataset. Effective propagation algorithms have previously been designed based on the graph-based semi-supervised learning framework. Therefore, these previous constraint propagation methods rely critically on a good similarity measure over the data points. Improper or noisy similarity measurements may dramatically degrade the performance of the constraint propagation algorithms. In this paper, we make attempt to exploit the available pairwise constraints to learn a new set of similarities, which are consistent with the supervisory information in the pairwise constraints, before propagating these initial constraints. Our method is a local learning algorithm. More specifically, we compute the similarities at each data point through simultaneously minimizing the local reconstruction error and local constraint error. The proposed method has been tested in the constrained clustering tasks on eight real-life datasets and then shown to achieve significant improvements with respect to the state of the arts.

Image Annotation by Graph-Based Inference With Integrated Multiple/Single Instance Representations

In most of the learning-based image annotation approaches, images are represented using multiple-instance (local) or single-instance (global) features. Their performances, however, are mixed as for certain concepts, the single-instance representations of images are more suitable, while for others, the multiple-instance representations are better. Thus this paper explores a unified learning framework that combines the multiple-instance and single-instance representations for image annotation. More specifically, we propose an integrated graph-based semi-supervised learning framework to utilize these two types of representations simultaneously. We further explore three strategies to convert from multiple-instance representation into a single-instance one. Experiments conducted on the COREL image dataset demonstrate the effectiveness and efficiency of the proposed integrated framework and the conversion strategies.

Video semantic analysis based on structure-sensitive anisotropic manifold ranking

As a major family of semi-supervised learning (SSL), graph-based SSL has recently attracted considerable interest in the machine learning community along with application areas such as video semantic analysis. In this paper, we analyze the connections between graph-based SSL and partial differential equation- (PDE) based diffusion. From the viewpoint of PDE-based diffusion, the label propagation in normal graph-based SSL is isotropic accompanied with distance. However, according to the structural assumption, which is one of the two basic assumptions in graph-based SSL, we need to enhance the label propagation between the samples in the same structure while weakening the counterpart between the samples in different structures. Accordingly, we deduce a novel graph-based SSL framework, named structure-sensitive anisotropic manifold ranking (SSAniMR), from PDE-based anisotropic diffusion. Instead of using Euclidean distance only, SSAniMR takes local structural difference into account to make the label propagation anisotropic, which is intrinsically different from the isotropic label propagation process in general graph-based SSL methods. Experiments conducted on the TREC Video Retrieval Evaluation (TRECVID) dataset show that this approach significantly outperforms existing graph-based SSL methods and is effective for video semantic annotation.