Dimensionality reduction is an important aspect in the pattern classification literature, and
linear discriminant analysis (LDA) is one of the most widely studied dimensionality reduction …

Recent critical commentaries unfavorably compare deep learning (DL) with standard
machine learning (SML) approaches for brain imaging data analysis. However, their …

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 …

Fault diagnosis of complex industrial processes becomes a challenging task due to various
fault patterns in sensor signals and complex interactions between different units. However …

Dimensionality reduction is a necessity in most hyperspectral imaging applications.
Tradeoffs exist between unsupervised statistical methods, which are typically based on …

This paper aims to take general tensors as inputs for supervised learning. A supervised
tensor learning (STL) framework is established for convex optimization based learning …

Subspace selection approaches are powerful tools in pattern classification and data
visualization. One of the most important subspace approaches is the linear dimensionality …

This paper develops an unsupervised discriminant projection (UDP) technique for
dimensionality reduction of high-dimensional data in small sample size cases. UDP can be …

Fisher linear discriminant analysis (LDA) is a classical subspace learning technique of
extracting discriminative features for pattern recognition problems. The formulation of the …

Linear discriminant analysis (LDA) is a well-known dimensionality reduction technique,
which is widely used for many purposes. However, conventional LDA is sensitive to outliers …