Electrocardiography (ECG) signals are often plagued by various types of noise, which substantially undermines the precision of subsequent analysis. This paper presents ECG-LLM, an innovative model designed to address the challenges posed by low-quality ECG signals. Leveraging the capabilities of Large Language Models (LLMs), ECG-LLM forecasts and imputes missing values in 12-lead ECG data. ECG-LLM adapts the auto-regressive characteristics of LLMs, utilizes textual markers for timestamp information, and employs a strategy of freezing Transformer layers while training new embedding and projection layers. We used ECG datasets from multiple sources for our experiments. Experimental results demonstrate that ECG-LLM significantly outperforms state-of-the-art time series forecasting models, achieving a Mean Squared Error (MSE) of 0.644 and a Mean Absolute Error (MAE) of 0.456 for a forecast length of 64. Additionally, after using ECG-LLM to restore the electrocardiogram, the disease recognition accuracy of the baseline model was improved. Our findings highlight the feasibility and effectiveness of applying LLMs to ECG signal processing, offering a new perspective for medical signal analysis and providing a potential new approach for signal preprocessing in healthcare. Code is available at https://github.com/dragonlfy/ECG-LLM.

Existing anchor graph-based semi-supervised classification methods can not adopt partial available labels of data to produce discriminative anchor graph, which is even challenging for incomplete multi-view data. Addressing above issues, a fast label prediction based on shrunk anchor graph (FLP-SAG) is designed for semi-supervised incomplete multi-view classification, which is capable of learning discriminative anchor graph iteratively. Firstly, in each view a similarity-based anchor graph is constructed and expanded to the size of complete data to align the multiple views. Then these pre-constructed anchor graphs are fused to get a common anchor graph, which is ready to be shrunk based on the predicted labels with high confidence scores in each iteration. To speed up the classification, an efficient two-step label prediction strategy is developed without the calculation of dense matrix inverse. Experimental results on four real world datasets comparing with several recently proposed methods demonstrate the superiority of the proposed method.

Multi-view clustering has attracted significant attention and application. Nonnegative matrix factorization is one popular feature of learning technology in pattern recognition. In recent years, many semi-supervised nonnegative matrix factorization algorithms were proposed by considering label information, which has achieved outstanding performance for multi-view clustering. However, most of these existing methods have either failed to consider discriminative information effectively or included too much hyper-parameters. Addressing these issues, a semi-supervised multi-view nonnegative matrix factorization with a novel fusion regularization (FRSMNMF) is developed in this article. In this work, we uniformly constrain alignment of multiple views and discriminative information among clusters with designed fusion regularization. Meanwhile, to align the multiple views effectively, two kinds of compensating matrices are used to normalize the feature scales of different views. Additionally, we preserve the geometry structure information of labeled and unlabeled samples by introducing the graph regularization simultaneously. Due to the proposed methods, two effective optimization strategies based on multiplicative update rules are designed. Experiments implemented on six real-world datasets have demonstrated the effectiveness of our FRSMNMF comparing with several state-of-the-art unsupervised and semi-supervised approaches.

Matrix factorization has demonstrated promising performance in the incomplete multiview clustering (IMC) tasks. However, many algorithms require feature normalization operations to ensure the stability of model results, so either the convergence is unstable, or the objective function cannot fit the data well. Addressing these issues, we propose a novel IMC algorithm using a normalizing alignment strategy (IMCNAS) based on nonnegative matrix factorization. Specifically, the columns of the basis matrices are constrained into unit vector space, which integrates the feature normalization and the optimizing process, and makes the model converge fast and stable. On the other hand, this enables the model to fit the data better and produce more reasonable factorization results. Further, we develop a novel pairwise co-regularization to align incomplete multiple views more directly, without introducing a common consensus matrix like traditional centroid-based co-regularization. Graph regularization is also incorporated in the proposed model to utilize the geometrical information of data. We implement IMCNAS with a centroid-based regularization and a pairwise co-regularization respectively, and leads to two variants, i.e., IMCNAS-1 and IMCNAS-2. Both variants are optimized with multiplicative updating rules. Extensive experiments conducted on various real-world datasets comparing several state-of-the-art IMC methods verified the effectiveness of the proposed methods. The source code is available at:https://github.com/GuoshengCui/IMCNAS.

Carotid artery lesions could be the pathology of subclinical atherosclerosis and hence lead to the onset of stroke. Early detection of abnormal carotid artery might help to better identify individuals susceptible to stroke. Considering the carotid artery ultrasonography is time-consuming and costly, the object of this paper is to establish a model to detect the status of carotid artery for preliminary screening of stroke, according to the simple physiological examination and survey information. However, most of the previous studies were based on the linear regression or the traditional machine learning methods, those suffer from two limitations. One is the limited labeled samples, and the other one is the missing data. To address these issues, we firstly propose a semi-supervised approach based on a modified variational autoencoder (VAE) to identify the abnormal carotid arteries. In this paper, a mixture of mean and K th nearest neighbours (MKNN) and a modified VAE were used for missing data imputation. The experimental results demonstrate that the proposed method can not only handle the missing values, but also outperform four widely used supervised approaches. Therefore, we can conclude that this semi-supervised model is a promising way to identify the abnormal carotid arteries.

Nonnegative matrix factorization (NMF) is one widely used feature extraction technology in the tasks of image clustering and image classification. For the former task, various unsupervised NMF methods based on the data distribution structure information have been proposed. While for the latter task, the label information of the dataset is one very important guiding. However, most previous proposed supervised NMF methods emphasis on imposing the discriminant constraints on the coefficient matrix. When dealing with new coming samples, the transpose or the pseudoinverse of the basis matrix is used to project these samples to the low dimension space. In this way, the label influence to the basis matrix is indirect. Although, there are also some methods trying to constrain the basis matrix in NMF framework, either they only restrict within-class samples or impose improper constraint on the basis matrix. To address these problems, in this article a novel NMF framework named discriminative and orthogonal subspace constraints-based nonnegative matrix factorization (DOSNMF) is proposed. In DOSNMF, the discriminative constraints are imposed on the projected subspace instead of the directly learned representation. In this manner, the discriminative information is directly connected with the projected subspace. At the same time, an orthogonal term is incorporated in DOSNMF to adjust the orthogonality of the learned basis matrix, which can ensure the orthogonality of the learned subspace and improve the sparseness of the basis matrix at the same time. This framework can be implemented in two ways. The first way is based on the manifold learning theory. In this way, two graphs, i.e., the intrinsic graph and the penalty graph, are constructed to capture the intra-class structure and the inter-class distinctness. With this design, both the manifold structure information and the discriminative information of the dataset are utilized. For convenience, we name this method as the name of the framework, i.e., DOSNMF. The second way is based on the Fisher’s criterion, we name it Fisher’s criterion-based DOSNMF (FDOSNMF). The objective functions of DOSNMF and FDOSNMF can be easily optimized using multiplicative update (MU) rules. The new methods are tested on five datasets and compared with several supervised and unsupervised variants of NMF. The experimental results reveal the effectiveness of the proposed methods.

Non-negative matrix factorization (NMF) has been one of the most popular methods for feature learning in the field of machine learning and computer vision. Most existing works directly apply NMF on high-dimensional image datasets for computing the effective representation of the raw images. However, in fact, the common essential information of a given class of images is hidden in their low rank parts. For obtaining an effective low-rank data representation, we in this paper propose a non-negative low-rank matrix factorization (NLMF) method for image clustering. For the purpose of improving its robustness for the data in a manifold structure, we further propose a graph regularized NLMF by incorporating the manifold structure information into our proposed objective function. Finally, we develop an efficient alternating iterative algorithm to learn the low-dimensional representation of low-rank parts of images for clustering. Alternatively, we also incorporate robust principal component analysis into our proposed scheme. Experimental results on four image datasets reveal that our proposed methods outperform four representative methods.

Nonnegative matrix factorization (NMF) is one of the most popular data representation methods in the field of computer vision and pattern recognition. High-dimension data are usually assumed to be sampled from the submanifold embedded in the original high-dimension space. To preserve the locality geometric structure of the data,k-nearest neighbor (k-NN) graph is often constructed to encode the near-neighbor layout structure. However,k-NN graph is based on Euclidean distance, which is sensitive to noise and outliers. In this article, we propose a refined-graph regularized nonnegative matrix factorization by employing a manifold regularized least-squares regression (MRLSR) method to compute the refined graph. In particular, each sample is represented by the whole dataset regularized with ℓ2-norm and Laplacian regularizer. Then a MRLSR graph is constructed based on the representative coefficients of each sample. Moreover, we present two optimization schemes to generate refined-graphs by employing a hard-thresholding technique. We further propose two refined-graph regularized nonnegative matrix factorization methods and use them to perform image clustering. Experimental results on several image datasets reveal that they outperform 11 representative methods.