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Methods of authentication of food grown in organic and conventional systems using chemometrics and data mining algorithms: A review
MD de Lima, R Barbosa - Food Analytical Methods, 2019 - Springer
There is a general consensus that the consumption of organic food can contribute to a
healthy diet; nevertheless, large-scale production of organic food is not an easy task since it …
healthy diet; nevertheless, large-scale production of organic food is not an easy task since it …
Pathological brain detection by artificial intelligence in magnetic resonance imaging scanning (invited review)
(Aim) Pathological brain detection (PBD) systems aim to assist and even replace
neuroradiologists to make decisions for patients. This review offers a comprehensive and …
neuroradiologists to make decisions for patients. This review offers a comprehensive and …
Semi-supervised local multi-manifold isomap by linear embedding for feature extraction
In this paper, we mainly propose a semi-supervised local multi-manifold Isomap learning
framework by linear embedding, termed SSMM-Isomap, that can apply the labeled and …
framework by linear embedding, termed SSMM-Isomap, that can apply the labeled and …
Supervised discriminant isomap with maximum margin graph regularization for dimensionality reduction
H Qu, L Li, Z Li, J Zheng - Expert Systems with Applications, 2021 - Elsevier
As one of the most popular nonlinear dimensionality reduction methods, Isomap has been
widely used in pattern recognition and machine learning. However, Isomap has the …
widely used in pattern recognition and machine learning. However, Isomap has the …
A manifold perspective on the statistical generalization of graph neural networks
Convolutional neural networks have been successfully extended to operate on graphs,
giving rise to Graph Neural Networks (GNNs). GNNs combine information from adjacent …
giving rise to Graph Neural Networks (GNNs). GNNs combine information from adjacent …
Low-rank preserving embedding
In this paper, we consider the problem of linear dimensionality reduction with the novel
technique of low-rank representation, which is a promising tool of discovering subspace …
technique of low-rank representation, which is a promising tool of discovering subspace …
Robust image hashing with isomap and saliency map for copy detection
Compression technology for representing image is on demand for efficiently processing
images in the Big Data era. Image hashing is an effective compression technology for …
images in the Big Data era. Image hashing is an effective compression technology for …
Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data
Background Microarray data have been widely utilized for cancer classification. The main
characteristic of microarray data is “large p and small n” in that data contain a small number …
characteristic of microarray data is “large p and small n” in that data contain a small number …
Unsupervised nonnegative adaptive feature extraction for data representation
In this paper, we propose a novel unsupervised Nonnegative Adaptive Feature Extraction
(NAFE) algorithm for data representation and classification. The formulation of NAFE …
(NAFE) algorithm for data representation and classification. The formulation of NAFE …
Granger causality driven AHP for feature weighted kNN
G Bhattacharya, K Ghosh, AS Chowdhury - Pattern Recognition, 2017 - Elsevier
The kNN algorithm remains a popular choice for pattern classification till date due to its non-
parametric nature, easy implementation and the fact that its classification error is bounded …
parametric nature, easy implementation and the fact that its classification error is bounded …