Providing a broad but in-depth introduction to neural network and machine learning in a
statistical framework, this book provides a single, comprehensive resource for study and …

In today's industrial systems, economic markets, life and health-care sciences fuzzy systems
play an important role in many application scenarios such as system identification, fault …

Linear Discriminant Analysis (LDA) has been a popular method for extracting features that
preserves class separability. The projection functions of LDA are commonly obtained by …

Feature selection is an important task in effective data mining. A new challenge to feature
selection is the so-called “small labeled-sample problem” in which labeled data is small and …

The linear discriminant analysis (LDA) is a very popular linear feature extraction approach.
The algorithms of LDA usually perform well under the following two assumptions. The first …

The increasing penetration of renewables into modern power systems is bringing new
oscillatory stability concerns. It is of significance to find all oscillatory modes concerned and …

Robust human gait recognition is challenging because of the presence of covariate factors
such as carrying condition, clothing, walking surface, etc. In this paper, we model the effect …

Dimensionality reduction methods have been successfully employed for face recognition.
Among the various dimensionality reduction algorithms, linear (Fisher) discriminant analysis …

Feature extraction plays an essential role in hyperspectral image classification.
Nonparametric feature extraction algorithms have more advantages than parametric ones …

Linear discriminant analysis (LDA) is a traditional statistical technique that reduces
dimensionality while preserving as much of the class discriminatory information as possible …