Systematic review of an automated multiclass detection and classification system for acute Leukaemia in terms of evaluation and benchmarking, open challenges …
This study aims to systematically review prior research on the evaluation and benchmarking
of automated acute leukaemia classification tasks. The review depends on three reliable …
of automated acute leukaemia classification tasks. The review depends on three reliable …
A review of the automated detection and classification of acute leukaemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open …
Context Acute leukaemia diagnosis is a field requiring automated solutions, tools and
methods and the ability to facilitate early detection and even prediction. Many studies have …
methods and the ability to facilitate early detection and even prediction. Many studies have …
A New Discriminative Sparse Representation Method for Robust Face Recognition via Regularization
Sparse representation has shown an attractive performance in a number of applications.
However, the available sparse representation methods still suffer from some problems, and …
However, the available sparse representation methods still suffer from some problems, and …
Towards Robust Discriminative Projections Learning via Non-Greedy -Norm MinMax
Linear Discriminant Analysis (LDA) is one of the most successful supervised dimensionality
reduction methods and has been widely used in many real-world applications. However, l 2 …
reduction methods and has been widely used in many real-world applications. However, l 2 …
A new formulation of linear discriminant analysis for robust dimensionality reduction
Dimensionality reduction is a critical technology in the domain of pattern recognition, and
linear discriminant analysis (LDA) is one of the most popular supervised dimensionality …
linear discriminant analysis (LDA) is one of the most popular supervised dimensionality …
Regularized label relaxation linear regression
Linear regression (LR) and some of its variants have been widely used for classification
problems. Most of these methods assume that during the learning phase, the training …
problems. Most of these methods assume that during the learning phase, the training …
Robust discriminant regression for feature extraction
Ridge regression (RR) and its extended versions are widely used as an effective feature
extraction method in pattern recognition. However, the RR-based methods are sensitive to …
extraction method in pattern recognition. However, the RR-based methods are sensitive to …
Rotational invariant dimensionality reduction algorithms
A common intrinsic limitation of the traditional subspace learning methods is the sensitivity to
the outliers and the image variations of the object since they use the L 2 norm as the metric …
the outliers and the image variations of the object since they use the L 2 norm as the metric …
Transfer learning based data feature transfer for fault diagnosis
W Xu, Y Wan, TY Zuo, XM Sha - IEEE Access, 2020 - ieeexplore.ieee.org
The development of sensor technology provides massive data for data-driven fault
diagnosis. In recent years, more and more scholars are studying artificial intelligence …
diagnosis. In recent years, more and more scholars are studying artificial intelligence …
Robust Sparse Hyperspectral Unmixing With Norm
Sparse unmixing (SU) of hyperspectral data have recently received particular attention for
analyzing remote sensing images, which aims at finding the optimal subset of signatures to …
analyzing remote sensing images, which aims at finding the optimal subset of signatures to …