Nassara Elhadji Ille Gado

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In this article, random matrix theory is used to propose a new K-means clustering algorithm via linear PCA. Our approach is devoted to linear PCA estimation when the number of the features d and the number of samples n go to infinity at the same rate. More precisely, we deal with the problem of building a consistent estimator of the eigenvectors of the(More)
Linear Discriminant Analysis (LDA) is a technique which is frequently used to extract discriminative features that preserve the class separability. LDA involves matrices eigen decomposition which can be computationally expensive in both time and memory, in particular when the number of samples and the number of features are large. This is the case for text(More)
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