Abnormal network traffic analysis has become an increasingly important research topic to
protect computing infrastructures from intruders. Yet, it is challenging to accurately discover …

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 …

A limited number of available training samples have become one bottleneck of face
recognition. In real-world applications, the face image might have various changes owing to …

Dimensionality reduction plays a significant role in high-dimensional data processing, and
Linear Discriminant Analysis (LDA) is a widely used supervised dimensionality reduction …

The number of wheat ears in the field is very important data for predicting crop growth and
estimating crop yield and as such is receiving ever-increasing research attention. To obtain …

Due to the multimodality of non-Gaussian data, traditional globality-preserved
dimensionality reduction (DR) methods, such as linear discriminant analysis (LDA) and …

As a well-known nonlinear dimensionality reduction method, Laplacian Eigenmaps (LE)
aims to find low dimensional representations of the original high dimensional data by …

Supplement to “Prediction models for network-linked data”. We provide the proof of
theoretical properties, computational complexity, additional simulation examples under …

Abstract This paper introduces Multiple Manifold Locally Linear Embedding (MM-LLE)
learning. This method learns multiple manifolds corresponding to multiple classes in a data …

Face images captured by surveillance cameras usually have low-resolution (LR) in addition
to uncontrolled poses and illumination conditions, all of which adversely affect the …