Sep 28, 2020 · This article proposes a novel feature extraction model with l2,1 norm constraint based on LDA, termed as RALDA. This model preserves within- ...

Linear discriminant analysis (LDA) is a well-known supervised method for dimensionality reduction in which the global structure of data can be preserved.

A novel feature extraction model with l2,1 norm constraint based on LDA, termed as RALDA, that preserves within-class local structure in the latent subspace ...

This article proposes a novel feature extractionmodel with l2,1 norm constraint based on LDA, termed as RALDA. This model preserves within-class local structure ...

Linear discriminant analysis (LDA) is a well-known supervised method for dimensionality reduction in which the global structure of data can be preserved.

Linear discriminant analysis (LDA) is awell-known supervisedmethod for dimensionality reduction in which the global structure of data can be preserved.

This paper describes a 3D measurement algorithm based on Multi-Frequency Phase Stepping, using a single period pattern and a higher frequency pattern.

Linear discriminant analysis (LDA) is a well-known supervised method for dimensionality reduction in which the global structure of data can be preserved.

Apr 15, 2023 · Our paper proposes a new feature extraction method, named as robust discriminant analysis (RDA), for data classification tasks.

A novel supervised dimensionality reduction method named Adaptive Local Linear Discriminant Analysis (ALLDA), which adaptively learns a k-nearest neighbors ...