This paper proposes a neighborhood linear discriminant analysis (nLDA) in which the scatter matrices are defined on a neighborhood consisting of reverse ...
Abstract. Linear Discriminant Analysis (LDA) assumes that all samples from the same class are independently and identically distributed (i.i.d.).
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Mar 10, 2023 · Currently, neighborhood linear discriminant analysis (nLDA) exploits reverse nearest neighbors (RNN) to avoid the assumption of linear ...
Sep 26, 2024 · Linear discriminant analysis (LDA) is a versatile statistical method for reducing redundant and noisy information from an original sample to its essential ...
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Linear Discriminant Analysis (LDA) assumes that all samples from the same class are independently and identically distributed (i.i.d.). LDA may fail in the ...
Neighborhood Components Analysis (NCA) tries to find a feature space such that a stochastic nearest neighbor algorithm will give the best accuracy.
The proposed framework includes two LLDA algorithms: a vector-based LLDA algorithm and a matrix-based LLDA (MLLDA) algorithm. MLLDA is directly applicable to ...
Nov 27, 2023 · Linear discriminant analysis (LDA) is an approach used in supervised machine learning to solve multi-class classification problems.
We use a local linear discriminant analysis to estimate an effective met- ric for computing neighborhoods. We determine the local decision boundaries from ...
Abstract—Linear discriminant analysis (LDA) is a well-known technique for supervised dimensionality reduction and has been extensively applied in many ...