Hyperspectral image (HSI) denoising is challenging not only because of the difficulty in preserving both spectral and spatial structures simultaneously, but also due to requirement removing various noises, which are often mixed together. In this paper, we present a nonconvex low rank matrix approximation (NonLRMA) model corresponding HSI method by reformulating problem using regularizer instead traditional nuclear norm, resulting tighter original sparsity-regularised function. NonLRMA aims...

Graph and subspace clustering methods have become the mainstream of multi-view due to their promising performance. However, (1) since graph learn graphs directly from raw data, when data is distorted by noise outliers, performance may seriously decrease; (2) use a "two-step" strategy representation affinity matrix independently, thus fail explore high correlation. To address these issues, we propose novel method via learning <underline xmlns:mml="http://www.w3.org/1998/Math/MathML"...

The low-rank tensor representation (LRTR) has become an emerging research direction to boost the multi-view clustering performance. This is because LRTR utilizes not only pairwise relation between data points, but also view of multiple views. However, there one significant challenge: uses nuclear norm as convex approximation provides a biased estimation rank function. To address this limitation, we propose generalized nonconvex (GNLTA) for subspace clustering. Instead correlation, GNLTA...

Low-rank matrix approximation (LRMA)-based methods have made a great success for grayscale image processing. When handling color images, LRMA either restores each channel independently using the monochromatic model or processes concatenation of three channels model. However, these two schemes may not make full use high correlation among RGB channels. To address this issue, we propose novel low-rank quaternion (LRQA) It contains major components: first, instead modeling pixel as scalar in...

Multi-view clustering refers to the task of partitioning numerous unlabeled multimedia data into several distinct clusters using multiple features. In this paper, we propose a novel nonlinear method called joint learning multi-view (JLMVC) jointly learn kernel representation tensor and affinity matrix. The proposed JLMVC has three advantages: (1) unlike existing low-rank representation-based methods that matrix in two separate steps, learns them both; (2) "kernel trick," can handle...

When used in engineering applications, most existing chaotic systems may have many disadvantages, including discontinuous parameter ranges, lack of robust chaos, and easy occurrence chaos degradation. In this article, we propose a two-dimensional (2-D) parametric polynomial system (2D-PPCS) as general that can yield 2-D maps with different exponent coefficient settings. The 2D-PPCS initializes two polynomials then applies modular chaotification to the polynomials. Setting control parameters...

Accurate segmentation of ventricle and myocardium from the late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is an important tool for myocardial infarction (MI) analysis. However, complex pattern LGE-CMR lack labeled samples make its automatic difficult to be implemented. In this paper, we propose unsupervised algorithm by using multiple style transfer networks data augmentation. It adopts two different perform easily available annotated balanced-Steady State Free Precession...

Therapeutic peptide prediction is important for the discovery of efficient therapeutic peptides and drug development. Researchers have developed several computational methods to identify different types. However, these focus on identifying some specific types peptides, failing predict comprehensive peptides. Moreover, it still challenging utilize properties peptides.In this study, an adaptive multi-view based tensor learning framework TPpred-ATMV proposed predicting constructs class...

Heart sound analysis plays an important role in early detecting heart disease. However, manual detection requires doctors with extensive clinical experience, which increases uncertainty for the task, especially medically underdeveloped areas. This paper proposes a robust neural network structure improved attention module automatic classification of wave. In preprocessing stage, noise removal Butterworth bandpass filter is first adopted, and then recordings are converted into time-frequency...

Some existing low-rank approximation approaches either need to predefine the rank values (such as matrix/tensor factorization-based methods) or fail consider local information of data (e.g., spatial spectral smooth structure). To overcome these drawbacks, this paper proposes a new model called tensor nuclear norm-based with total variation regularization (TLR-TV) for color and multispectral image denoising. TLR-TV uses norm encode global prior preserve spatial-spectral continuity in unified...

Multiview clustering as an important unsupervised method has been gathering a great deal of attention. However, most multiview methods exploit the self-representation property to capture relationship among data, resulting in high computation cost calculating coefficients. In addition, they usually employ different regularizers learn representation tensor or matrix from which transition probability is constructed separate step, such one proposed by Wu et al.. Thus, optimal cannot be...

The remote sensing scene images classification has been of great value to civil and military fields. Deep learning models, especially the convolutional neural network (CNN), have achieved success in this task, however, they may suffer from two challenges: firstly, sizes category objects are usually different, but conventional CNN extracts features with fixed convolution extractor which could cause failure multi-scale features; secondly, some image regions not be useful during feature...

Multi-view subspace clustering aims to utilize the comprehensive information of multi-source features aggregate data into multiple subspaces. Recently, low-rank tensor learning has been applied multi-view clustering, which explores high-order correlations and achieved remarkable results. However, these existing methods have certain limitations: 1) The processes label indicator matrix are independent. 2) Variable contributions different views consistent results not discriminated. To handle...

With the popularity of wireless body sensor network, real-time and continuous collection single-lead electrocardiogram (ECG) data becomes possible in a convenient way. Data mining from collected ECG waves has therefore aroused extensive attention worldwide, where early detection atrial fibrillation (AF) is hot research topic. In this paper, two-channel convolutional neural network combined with augmentation method proposed to detect AF short recordings. It consists three modules, first...