Results 1 to 10 of about 169,803 (261)
Hierarchical Discriminant Analysis [PDF]
Sensors, 2018 The Internet of Things (IoT) generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification) is very difficult, so it requires excellent subspace learning algorithms ...
Di Lu +3 more
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Reversible Discriminant Analysis
IEEE Access, 2018 Principal component analysis (PCA) and linear discriminant analysis (LDA) have been extended to be a group of classical methods in dimensionality reduction for unsupervised and supervised learning, respectively.
Lan Bai +3 more
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Sparse Discriminant Analysis [PDF]
Technometrics, 2011 We consider the problem of performing interpretable classification in the high-dimensional setting, in which the number of features is very large and the number of observations is limited. This setting has been studied extensively in the chemometrics literature, and more recently has become commonplace in biological and medical applications.
Line H Clemmensen +2 more
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Milk yield prediction in Friesian cows using linear and flexible discriminant analysis under assumptions violations [PDF]
BMC Veterinary ResearchBackground The application of novel technologies is now widely used to assist in making optimal decisions. This study aimed to evaluate the performance of linear discriminant analysis (LDA) and flexible discriminant analysis (FDA) in classifying and ...
Sherif A. Moawed +4 more
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IEEE Access, 2023 Recently, an absolute value inequalities discriminant analysis criterion with robustness and sparseness for supervised dimensionality reduction was studied.
Chun-Na Li +4 more
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Ekuilibrium: Jurnal Ilmiah Bidan Ilmu Ekonomi, 2020 This study aims to determine whether there are clear differences between groups on the dependent variable. This analysis uses the independent variable Life Expectancy (X1), Number of Health Facilities (Puskesmas) (X2), Number of Facilities (Supporting ...
Ramli Lewenussa, Rais Dera Pua Rawi
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Discriminant Analysis for Correlated Data [PDF]
Maǧallaẗ Al-Buḥūṯ Al-Mālīyyaẗ wa Al-Tiğāriyyaẗ, 2019 The correlated data are of great importance in the practical life, for example we may want to track the case of a patient after taking a treatment for consecutive periods of time. Also, we may want to track a disease with the members of a certain family.
Ahmed Mohamed Mohamed El-Sayed
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Quadratic Multilinear Discriminant Analysis for Tensorial Data Classification
Algorithms, 2023 Over the past decades, there has been an increase of attention to adapting machine learning methods to fully exploit the higher order structure of tensorial data.
Cristian Minoccheri +4 more
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Wasserstein discriminant analysis [PDF]
Machine Learning, 2018 Wasserstein Discriminant Analysis (WDA) is a new supervised method that can improve classification of high-dimensional data by computing a suitable linear map onto a lower dimensional subspace. Following the blueprint of classical Linear Discriminant Analysis (LDA), WDA selects the projection matrix that maximizes the ratio of two quantities: the ...
Flamary, Rémi +3 more
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Probabilistic Class-Specific Discriminant Analysis
IEEE Access, 2020 In this paper we formulate a probabilistic model for class-specific discriminant subspace learning. The proposed model can naturally incorporate the multi-modal structure of the negative class, which is neglected by existing class-specific methods ...
Alexandros Iosifidis
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