In this paper, several two-dimensional extensions of principal component analysis (PCA) and linear discriminant analysis (LDA) techniques has been applied in a lossless dimensionality reduction framework, for face recognition application. In this framework, the benefits of dimensionality reduction were used to improve the performance of its predictive model, which was a support vector machine (...

anomaly detection (ad) has recently become an important application of target detection in hyperspectral images. the reed-xialoi (rx) is the most widely used ad algorithm that suffers from “small sample size” problem. the best solution for this problem is to use dimensionality reduction (dr) techniques as a pre-processing step for rx detector. using this method not only improves the detection p...

anomaly detection (ad) has recently become an important application of hyperspectral images analysis. the goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. one way to improve the performance and runtime of these algorithms is to use dimensionality reduction (dr) techniques. this paper evaluates the effect of thr...

objective: diabetes is one of the most common metabolic diseases. earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...

In this paper, we present a randomized singular value decomposition (rSVD) method for the purposes of lossless compression, reconstruction, classification, and target detection with hyperspectral (HSI) data. Recent work in low-rank matrix approximations obtained from random projections suggest that these approximations are well-suited for randomized dimensionality reduction. Approximation error...

Dimensionality reduction studies methods that effectively reduce data dimensionality for efficient data processing tasks such as pattern recognition, machine learning, text retrieval, and data mining. We introduce the field of dimensionality reduction by dividing it into two parts: feature extraction and feature selection. Feature extraction creates new features resulting from the combination o...

Space-time is a profound concept in physics. This concept was shown to be useful for dimensionality reduction. We present basic definitions with interesting counter-intuitions. We give theoretical propositions to show that space-time is a more powerful representation than Euclidean space. We apply this concept to manifold learning for preserving local information. Empirical results on nonmetric...