Hyperspectral unmixing is a crucial preprocessing step for material classification and
recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions …

It is well-known that the applicability of linear discriminant analysis (LDA) to high-
dimensional pattern classification tasks such as face recognition often suffers from the so …

In this paper, we propose a novel ensemble-based approach to boost performance of
traditional Linear Discriminant Analysis (LDA)-based methods used in face recognition. The …

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 …

Recent advances have shown that algorithms with (2D) matrix-based representation perform
better than the traditional (1D) vector-based ones. In particular, 2D-LDA has been widely …

With the massive explosion of machine learning in our day-to-day life, incremental and
adaptive learning has become a major topic, crucial to keep up-to-date and improve …

The classification of upper-limb movements based on surface electromyography (EMG)
signals is an important issue in the control of assistive devices and rehabilitation systems …

This paper addresses the dimension reduction problem in Fisherface for face recognition.
When the number of training samples is less than the image dimension (total number of …

Matrices, or more generally, multi-way arrays (tensors) are common forms of data that are
encountered in a wide range of real applications. How to classify this kind of data is an …