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Found 10 papers, 3 papers with code
CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network
In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues.
Deep Double Incomplete Multi-view Multi-label Learning with Incomplete Labels and Missing Views
View missing and label missing are two challenging problems in the applications of multi-view multi-label classification scenery.
Highly Confident Local Structure Based Consensus Graph Learning for Incomplete Multi-View Clustering
Graph-based multi-view clustering has attracted extensive attention because of the powerful clustering-structure representation ability and noise robustness.
A Survey on Incomplete Multi-view Clustering
However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failure of the conventional multi-view clustering methods.
Deep Learning on Image Denoising: An overview
However, there are substantial differences in the various types of deep learning methods dealing with image denoising.
Forensic shoe-print identification: a brief survey
As an advanced research topic in forensics science, automatic shoe-print identification has been extensively studied in the last two decades, since shoe marks are the clues most frequently left in a crime scene.
Adaptive Locality Preserving Regression
This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification.
Enhanced CNN for image denoising
Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising.
Deep Learning for Image Denoising: A Survey
Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technology has received a great deal of attention and has been widely applied in the field of imaging processing.
Local Multiple Directional Pattern of Palmprint Image
Conventional methods usually capture the only one of the most dominant direction of palmprint images.
