Results 11 to 20 of about 536,157 (280)
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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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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Discriminative cluster analysis [PDF]
Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006 Clustering is one of the most widely used statistical tools for data analysis. Among all existing clustering techniques, k-means is a very popular method because of its ease of programming and because it accomplishes a good trade-off between achieved performance and computational complexity.
Fernando De la Torre, Takeo Kanade
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Discriminant analysis of spatial-temporal data
Lietuvos Matematikos Rinkinys, 2002 There is not abstract.
Jūratė Šaltytė-Benth +1 more
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Sugarcane yield forecast using weather based discriminant analysis
Smart Agricultural Technology, 2023 Discriminant function analysis has been used for forecasting of Sugarcane yield of Coimbatore district in Tamilnadu. Crop yield has been classified into two and three groups.
S R Krishan Priya +3 more
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Sparse multinomial kernel discriminant analysis (sMKDA) [PDF]
, 2009 Dimensionality reduction via canonical variate analysis (CVA) is important for pattern recognition and has been extended variously to permit more flexibility, e.g. by "kernelizing" the formulation.
Abe +40 more
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Dynamic Linear Discriminant Analysis in High Dimensional Space [PDF]
, 2019 High-dimensional data that evolve dynamically feature predominantly in the modern data era. As a partial response to this, recent years have seen increasing emphasis to address the dimensionality challenge.
Chen, Ziqi, Jiang, Binyan, Leng, Chenlei
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Alternating direction method of multipliers for penalized zero-variance discriminant analysis [PDF]
, 2015 We consider the task of classification in the high dimensional setting where the number of features of the given data is significantly greater than the number of observations.
Ames, Brendan +2 more
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Logistic discriminant analysis [PDF]
2009 IEEE International Conference on Systems, Man and Cybernetics, 2009 Linear discriminant analysis (LDA) is one of the well known methods to extract the best features for the multi-class discrimination. Otsu derived the optimal nonlinear discriminant analysis (ONDA) by assuming the underlying probabilities and showed that the ONDA was closely related to Bayesian decision theory (the posterior probabilities).
Takio Kurita +2 more
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