Abstract Linear Discriminant Analysis (LDA) assumes that all samples from the same class
are independently and identically distributed (iid). LDA may fail in the cases where the …

The classical linear discriminant analysis (LDA) algorithm has three primary drawbacks, ie,
small sample size problem, sensitivity to noise and outliers, and inability to deal with multi …

Linear discriminant analysis (LDA) is a very popular supervised feature extraction method
and has been extended to different variants. However, classical LDA has the following …

Nowadays, radio broadcasting plays an important role in people's daily life. However,
unauthorized broadcasting stations may seriously interfere with normal broadcastings and …

Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative
features for multi-class classification. A vast majority of existing LDA algorithms are prone to …

Linear discriminant analysis (LDA) is a versatile statistical method for reducing redundant
and noisy information from an original sample to its essential features. Particularly, LDA is a …

Linear discriminant analysis (LDA) is one of the most important supervised linear
dimensional reduction techniques which seeks to learn low-dimensional representation from …

Linear discriminant analysis (LDA) as a classical supervised dimensionality reduction
method has shown powerful capability in various image classification tasks. The purpose of …

To defy the curse of dimensionality, the inputs are always projected from the original high-
dimensional space into the target low-dimension space for feature extraction. However, due …

Linear discriminant analysis (LDA) is one of the most successful feature extraction methods,
which projects high-dimensional data to a low-dimensional space with discriminative …