A typical problem in Thermal Nondestructive Testing/Evaluation (TNDT/E) is that of unsupervised feature extraction from the experimental data. Matrix factorization methods (MFMs) are mathematical techniques well suited for this task. In this paper we present the application of three MFMs: Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), and Archetypal Analysis (AA). ...

In this paper, the method of kernel Fisher discriminant (KFD) is analyzed and its nature is revealed, i.e., KFD is equivalent to kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). Based on this result, a more transparent KFD algorithm is proposed. That is, KPCA is ;rst performed and then LDA is used for a second feature extraction in the KPCA-transformed ...

Human gait recognition is a developing biometric engineering now a days. It perceives the individual from its walk and above all from a distance without subject’s cooperation. As human gait recognition system is influenced by diverse view variations effects. So, in this paper we have proposed a human gait recognition strategy for the images caught from distinctive viewing edges (0, 45, 90 degre...

q-mode hierarchical cluster (hca) and principal component analysis (pca) were simultaneously applied to groundwater hydrochemical data from the three times in 2004: june, september, and december, along the ain azel aquifer, algeria, to extract principal factors corresponding to the different sources of variation in the hydrochemistry, with the objective of defining the main controls on the h...

targeted extension for heterogeneous farming systems is a challenge in developing countries. farm type identification and characterization based on estimates of income from different farm components allows simplifying diversity in farming systems. use of multivariate statistical techniques, such as principal component analysis (pca) and cluster analysis (ca), help in such farm typology delineat...

classical lbp such as complexity and high dimensions of feature vectors that make it necessary to apply dimension reduction processes. in this paper, we introduce an improved lbp algorithm to solve these problems that utilizes fast pca algorithm for reduction of vector dimensions of extracted features. in other words, proffer method (fast pca+lbp) is an improved lbp algorithm that is extracted ...

the karnafully is one of the most important rivers due to its profound influence on water chemistry and sediment characteristics. the present study intended to assess the quality of water and sediment from intertidal zone of this river in respect to the pollution index. seasonal water and sediment samples were collected during four seasons (monsoon, post-monsoon, winter, and pre-monsoon) of 201...

Feature extraction plays an important role in machine learning for signal processing, particularly low-dimensional data visualization and predictive analytics. Data from real-world complex systems are often high-dimensional, multi-scale, non-stationary. Extracting key features of this type is challenging. This work proposes a novel approach to analyze Epileptic EEG signals using both wavelet po...