This paper aims to conduct a study on the listwise approach learning rank. The learns ranking function by taking individual lists as instances and minimizing loss defined predicted list ground-truth list. Existing work mainly focused development of new algorithms; methods such RankCosine ListNet have been proposed good performances them observed. Unfortunately, underlying theory was not sufficiently studied so far. To amend problem, this proposes conducting theoretical analysis rank...

Low rank matrix approximation (LRMA), which aims to recover the underlying low from its degraded observation, has a wide range of applications in computer vision. The latest LRMA methods resort using nuclear norm minimization (NNM) as convex relaxation nonconvex minimization. However, NNM tends over-shrink components and treats different equally, limiting flexibility practical applications. We propose more flexible model, namely Weighted Schatten $p$-Norm Minimization (WSNM), generalize...

The Internet-of-Things (IoT) technology is widely used in various fields. In the Earth observation system, hyperspectral images (HSIs) are acquired by sensors and always transmitted to cloud for analysis. order reduce cost reply promptly, we deploy artificial intelligence (AI) models data analysis on edge servers. Subspace clustering, core of AI model, employed analyze high-dimensional image such as HSIs. However, most traditional subspace clustering algorithms construct a single which can...

In this paper, we address the multiview nonlinear subspace representation problem. Traditional learning methods assume that heterogeneous features of data usually lie within union multiple linear subspaces. However, instead subspaces, feature actually resides in subspaces many real-world applications, resulting unsatisfactory clustering performance. To overcome this, propose a hyper-Laplacian regularized multilinear self-representation model, which is referred to as HLR-M <sup...

The fifth generation (5G) mobile communication technology brings people a higher perceived rate experience, the high-quality service of high-density user connection, and other commercial applications. As an important means data processing in 5G heterogeneous networks (HetNets), fusion is faced with large number malicious code attacks. Thus, it particularly to find efficient detection method. However, traditional research, due dataset imbalance, complexity deep learning network model, use...

Diabetic retinopathy (DR) is a severe ocular complication of diabetes that can lead to vision damage and even blindness. Currently, traditional deep convolutional neural networks (CNNs) used for DR grading tasks face two primary challenges: (1) insensitivity minority classes due imbalanced data distribution, (2) neglecting the relationship between left right eyes by utilizing fundus image only one eye training without differentiating them. To tackle these challenges, we proposed DRGCNN (DR...

Hyperspectral images (HSIs) are inevitably corrupted by mixture noise during their acquisition process, in which various kinds of noise, e.g., Gaussian impulse dead lines, and stripes, may exist concurrently. In this paper, removal is well illustrated the task recovering low-rank sparse components a given matrix, constructed stacking vectorized HSI patches from all bands at same position. Instead applying traditional nuclear norm, nonconvex regularizer, i.e., weighted Schatten p-norm (WSN),...

A bat algorithm (BA) is a heuristic that operates by imitating the echolocation behavior of bats to perform global optimization. The BA widely used in various optimization problems because its excellent performance. In algorithm, search capability determined parameter loudness and frequency. However, experiments show each operator can only improve performance at certain time. this paper, novel with multiple strategies coupling (mixBA) proposed solve problem. To prove effectiveness we...

This paper presents a novel deep learning framework for the inter-patient electrocardiogram (ECG) heartbeat classification. A symbolization approach especially designed ECG is introduced, which can jointly represent morphology and rhythm of alleviate influence variation through baseline correction. The symbolic representation used by multi-perspective convolutional neural network (MPCNN) to learn features automatically classify heartbeat. We evaluate our method detection supraventricular...

We propose a robust tracking algorithm based on local sparse coding with discriminative dictionary learning and new keypoint matching schema. This consists of two parts: the online updated for (SOD part), refinement enhancing performance (KP part). In SOD part, image patches target object background are represented by their codes using an over-complete dictionary. Such dictionary, which encodes information both foreground background, may provide more power. Furthermore, in order to adapt...

In this paper, a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> eep xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ulti-view xmlns:xlink="http://www.w3.org/1999/xlink">J</b> oint xmlns:xlink="http://www.w3.org/1999/xlink">C</b> lustering ( xmlns:xlink="http://www.w3.org/1999/xlink">DMJC</b> ) framework is proposed, where multiple deep embedded features, multi-view fusion mechanism, and clustering assignments can be learned...

In this article, we propose a multiview self-representation model for nonlinear subspaces clustering. By assuming that the heterogeneous features lie within union of multiple linear subspaces, recent subspace learning methods aim to capture complementary and consensus from views boost performance. However, in real-world applications, data feature usually resides leading undesirable results. To end, kernelized version tensor-based clustering, which is referred as Kt-SVD-MSC, jointly learn...

Abstract Recommendation system is a technology that can mine user's preference for items. Explainable recommendation to produce recommendations target users and give reasons at the same time reveal recommendations. The explainability of improve transparency probability choosing recommended merits about are obvious, but it not enough focus solely on in field explainable Therefore, essential construct an framework items while maintaining accuracy diversity. An based knowledge graph...