We propose a novel discriminative feature learning method via Max-Min Ratio Analysis
(MMRA) for exclusively dealing with the long-standing “worst-case class separation” …

Feature selection and feature extraction, in the field of data dimensionality reduction, are the
two main strategies. Nevertheless, each of these two strategies has its own advantages and …

The linear discriminant analysis (LDA) method needs to be transformed into another form to
acquire an approximate closed-form solution, which could lead to the error between the …

Dimension reduction is a critical technology for high-dimensional data processing, where
Linear Discriminant Analysis (LDA) and its variants are effective supervised methods …

The majority of the classical dimensionality reduction methods can be unified into a graph-
embedding-based framework. A fixed graph constructed in a high-dimensional space has …

The high dimensionality of real-life datasets is one of the biggest challenges in the machine
learning field. Due to the increased need for computational resources, the higher the …

We propose a new supervised dimensionality reduction technique called Supervised Linear
Centroid-Encoder (SLCE), a linear counterpart of the nonlinear Centroid-Encoder …

Linear discriminant analysis (LDA) is a well-known supervised method that can perform
dimensionality reduction and feature extraction effectively. However, traditional LDA-based …

Spectral-based subspace learning is a common data preprocessing step in many machine
learning pipelines. The main aim is to learn a meaningful low dimensional embedding of the …

With the rapid advancement in the science domain, the explosion of available data is seen
in recent years. Laboratory instruments are becoming more advanced day by day and are …