One of the main applications of microarray technology is to determine the gene expression profiles of diseases and disease treatments. This is typically done by selecting a small number of genes from amongst thousands to tens of thousands, whose expression values are collectively used as classification profiles. This gene selection process is notoriously challenging because microarray data norm...

Discriminative dictionary learning aims to learn a dictionary from training samples to enhance the discriminative capability of their coding vectors. Several discrimination terms have been proposed by assessing the prediction loss (e.g., logistic regression) or class separation criterion (e.g., Fisher discrimination criterion) on the coding vectors. In this paper, we provide a new insight on di...

Dictionary learning (DL) for sparse coding based classification has been widely researched in pattern recognition in recent years. Most of the DL approaches focused on the reconstruction performance and the discriminative capability of the learned dictionary. This paper proposes a new method for learning discriminative dictionary for sparse representation based classification, called Incoherent...

A discriminatory dataset refers to a dataset with undesirable correlation between sensitive attributes and the class label, which often leads to biased decision making in data analytics processes. This paper investigates how to build discrimination-aware models even when the available training set is intrinsically discriminating based on some sensitive attributes, such as race, gender or person...

in order to assess the satellite data for soil investigation, aster digital data 20 june 2006, field study and phisiochemical properties of soil, were analyzed. all landcover classes including soils are classified based onmorphological and physico-chemical characteristics. images were geocorrected and photomorphic units were selected based upon visual interpretation and sampling in study area. ...