منابع مشابه
Deep Linear Discriminant Analysis
We introduce Deep Linear Discriminant Analysis (DeepLDA) which learns linearly separable latent representations in an end-to-end fashion. Classic LDA extracts features which preserve class separability and is used for dimensionality reduction for many classification problems. The central idea of this paper is to put LDA on top of a deep neural network. This can be seen as a non-linear extension...
متن کاملFisher Linear Discriminant Analysis
Fisher Linear Discriminant Analysis (also called Linear Discriminant Analysis(LDA)) are methods used in statistics, pattern recognition and machine learning to find a linear combination of features which characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later c...
متن کاملSeparable linear discriminant analysis
Linear discriminant analysis (LDA) is a popular technique for supervised dimension reduction. Due to the curse of dimensionality usually suffered by LDA when applied to 2D data, several two-dimensional LDA (2DLDA) methods have been proposed in recent years. Among which, the Y2DLDA method, introduced by Ye et al. (2005), is an important development. The idea is to utilize the underlying 2D data ...
متن کاملGeometric linear discriminant analysis
When it becomes necessary to reduce the complexity of a classifier, dimensionality reduction can be an effective way to address classifier complexity. Linear Discriminant Analysis (LDA) is one approach to dimensionality reduction that makes use of a linear transformation matrix. The widely used Fisher’s LDA is “sub-optimal” when the sample class covariance matrices are unequal, meaning that ano...
متن کاملLinear Discriminant Analysis Algorithms
We propose new algorithms for computing linear discriminants to perform data dimensionality reduction from R to R, with p < n. We propose alternatives to the classical Fisher’s Distance criterion, namely, we investigate new criterions based on the: Chernoff-Distance, J-Divergence and Kullback-Leibler Divergence. The optimization problems that emerge of using these alternative criteria are non-c...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SCIENTIA SINICA Informationis
سال: 2021
ISSN: 1674-7267
DOI: 10.1360/ssi-2019-0175