VSSA-NET: Vertical spatial sequence attention network for traffic sign detection
Although traffic sign detection has been studied for years and great progress has been
made with the rise of deep learning technique, there are still many problems remaining to be …
made with the rise of deep learning technique, there are still many problems remaining to be …
Joint and progressive subspace analysis (JPSA) with spatial–spectral manifold alignment for semisupervised hyperspectral dimensionality reduction
Conventional nonlinear subspace learning techniques (eg, manifold learning) usually
introduce some drawbacks in explainability (explicit mapping) and cost effectiveness …
introduce some drawbacks in explainability (explicit mapping) and cost effectiveness …
A survey on sparse learning models for feature selection
X Li, Y Wang, R Ruiz - IEEE transactions on cybernetics, 2020 - ieeexplore.ieee.org
Feature selection is important in both machine learning and pattern recognition.
Successfully selecting informative features can significantly increase learning accuracy and …
Successfully selecting informative features can significantly increase learning accuracy and …
Feature and subfeature selection for classification using correlation coefficient and fuzzy model
This article presents an analysis of data extraction for classification using correlation
coefficient and fuzzy model. Several traditional methods of data extraction are used for …
coefficient and fuzzy model. Several traditional methods of data extraction are used for …
Learning modality-consistency feature templates: A robust RGB-infrared tracking system
With a large number of video surveillance systems installed for the requirement from
industrial security, the task of object tracking, which aims to locate objects of interest in …
industrial security, the task of object tracking, which aims to locate objects of interest in …
[HTML][HTML] Unsupervised deep noise modeling for hyperspectral image change detection
Hyperspectral image (HSI) change detection plays an important role in remote sensing
applications, and considerable research has been done focused on improving change …
applications, and considerable research has been done focused on improving change …
[HTML][HTML] Fast spectral clustering for unsupervised hyperspectral image classification
Hyperspectral image classification is a challenging and significant domain in the field of
remote sensing with numerous applications in agriculture, environmental science …
remote sensing with numerous applications in agriculture, environmental science …
Robust and sparse principal component analysis with adaptive loss minimization for feature selection
Principal component analysis (PCA) is one of the most successful unsupervised subspace
learning methods and has been used in many practical applications. To deal with the …
learning methods and has been used in many practical applications. To deal with the …
Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images
Abstract Background and Objective Non-invasively predicting the risk of cancer metastasis
before surgery can play an essential role in determining which patients can benefit from …
before surgery can play an essential role in determining which patients can benefit from …
Fuzzy rough sets-based incremental feature selection for hierarchical classification
W Huang, Y She, X He, W Ding - IEEE Transactions on Fuzzy …, 2023 - ieeexplore.ieee.org
In the era of big data, both the size and the number of features, samples, and classes
continue to increase, resulting in high-dimensional classification tasks. One characteristic …
continue to increase, resulting in high-dimensional classification tasks. One characteristic …