Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Survey on multi-output learning
The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …
It is an important learning problem for decision-making since making decisions in the real …
Data-driven graph construction and graph learning: A review
A graph is one of important mathematical tools to describe ubiquitous relations. In the
classical graph theory and some applications, graphs are generally provided in advance, or …
classical graph theory and some applications, graphs are generally provided in advance, or …
Multi-view low-rank sparse subspace clustering
Most existing approaches address multi-view subspace clustering problem by constructing
the affinity matrix on each view separately and afterwards propose how to extend spectral …
the affinity matrix on each view separately and afterwards propose how to extend spectral …
Identifying autism spectrum disorder with multi-site fMRI via low-rank domain adaptation
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is characterized by a
wide range of symptoms. Identifying biomarkers for accurate diagnosis is crucial for early …
wide range of symptoms. Identifying biomarkers for accurate diagnosis is crucial for early …
Locality and structure regularized low rank representation for hyperspectral image classification
Hyperspectral image (HSI) classification, which aims to assign an accurate label for
hyperspectral pixels, has drawn great interest in recent years. Although low-rank …
hyperspectral pixels, has drawn great interest in recent years. Although low-rank …
Infrared small target detection via self-regularized weighted sparse model
Infrared search and track (IRST) system is widely used in many fields, however, it's still a
challenging task to detect infrared small targets in complex background. This paper …
challenging task to detect infrared small targets in complex background. This paper …
Sparse low-rank multi-view subspace clustering with consensus anchors and unified bipartite graph
Anchor technology is popularly employed in multi-view subspace clustering (MVSC) to
reduce the complexity cost. However, due to the sampling operation being performed on …
reduce the complexity cost. However, due to the sampling operation being performed on …
Feature selective projection with low-rank embedding and dual Laplacian regularization
Feature extraction and feature selection have been regarded as two independent
dimensionality reduction methods in most of the existing literature. In this paper, we propose …
dimensionality reduction methods in most of the existing literature. In this paper, we propose …
Adaptive weighted dictionary representation using anchor graph for subspace clustering
Samples are commonly represented as sparse vectors in many dictionary representation
algorithms. However, this method may result in loss of discriminatory information. Moreover …
algorithms. However, this method may result in loss of discriminatory information. Moreover …
Deep domain generalization with structured low-rank constraint
Domain adaptation nowadays attracts increasing interests in pattern recognition and
computer vision field, since it is an appealing technique in fighting off weakly labeled or …
computer vision field, since it is an appealing technique in fighting off weakly labeled or …