In this article, a comprehensive review of the state-of-art graph-based learning methods for
classification of the hyperspectral images (HSI) is provided, including a spectral information …

Many of today's drug discoveries require expertise knowledge and insanely expensive
biological experiments for identifying the chemical molecular properties. However, despite …

Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data
and separate the interclass data, and it is very useful to analyze the high-dimensional data …

Supervised classification methods have been widely used in the hyperspectral remote
sensing image analysis. However, they require a large number of training samples to …

Active learning (AL) and semisupervised learning (SSL) are both promising solutions to
hyperspectral image classification. Given a few initial labeled samples, this work combines …

We present a novel semi-supervised algorithm for classification of hyperspectral data from
remote sensors. Our method is inspired by the Tracking-Learning-Detection (TLD) …

Hyperspectral unmixing (HU) plays a fundamental role in a wide range of hyperspectral
applications. It is still challenging due to the common presence of outlier channels and the …

Hyperspectral imagery (HSI) classification, which attempts to assign hyperspectral pixels
with proper labels, has drawn significant attention in various applications. Recently, the …

Due to the popularity of smartphones and wearable devices nowadays, mobile health
(mHealth) technologies are promising to bring positive and wide impacts on people's health …

Piecewise linear regressions have shown many successful applications in image denoising,
signal process, and data mining fields. In essence, they attempt to seek multiple linear …