Principal component analysis (PCA) is a powerful technique for extracting structure from possibly highdimensional data sets, while kernel PCA (KPCA) is the application of PCA in a kernel-defined feature space. For standard PCA and KPCA, if the size of dataset is large, it will need a very large memory to store kernel matrix and a lot of time to calculate eigenvalues and corresponding eigenvecto...

The paper presents an automatic classification system, which discriminates the diferent types of'siniglelayered clouds uising Principal Component Anialysis (PCA) with enhanced accuracy as compared to other techniques. PCA is an image classification techniqute typically used for ace recognition. Principal components are the distinctive or peculiar featuires of an image. The approach described in...

Facial Expression Recognition is one of the active research area in the field of Human Machine Interaction (HMI) because of its several applications such as human emotion analysis, stress level and lie detection. In this paper, an algorithm for facial expression recognition has been proposed which integrate the Local Binary Patterns (LBP), Gabor filter and Principal Component Analysis (PCA). Th...

This paper proposes a novel approach using two-dimensional principal component analysis (2D-PCA) and local direction descriptor for face recognition. The proposed method utilizes the transformed image obtained from local direction descriptor as the direct input image of 2D-PCA algorithms. The performance comparison was performed using principal component analysis (PCA) and Gabor-wavelets based ...

We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data population. We show that replacing intensities with gradient orientations and the l2 norm with a ...

With the growth of information technology there is a greater need of high security, so biometric authentication systems are gaining importance. Face recognition is more used because it’s easy and non intrusive method during acquisition procedure. Various methods are used for facial recognition. Principal component analysis (PCA) based systems are used often. In this paper we study 4 distance me...

With the increasing of non-linear, burst or un-balanced load, power quality issues in the grid is becoming important. With more power quality monitors installed with higher sampling rates, an expanded size of power quality data brings difficulty to storage, transmission and analysis. In this paper, principal component analysis (PCA), which is a popular feature extraction algorithm in pattern re...

This paper considers the development of reduced chemistry models for high enthalpy and plasma flows using Principal Component Analysis (PCA) based methods. Starting from detailed chemistry models, such as multi-temperature and collisional-radiative formulations, a reduction of the variable set (species mass fractions and temperatures) is proposed by projecting the full set on a reduced basis ma...

Principal components analysis (PCA) is the optimal linear encoder of data. Sparse linear encoders (e.g., sparse PCA) produce more interpretable features that can promote better generalization. (i) Given a level of sparsity, what is the best approximation to PCA? (ii) Are there efficient algorithms which can achieve this optimal combinatorial tradeoff? We answer both questions by providing the f...

We propose shiny analysis framework accompanied with makeup deterioration using normalized facial images (MaVIC and the corresponding makeup-deteriorated data sets). These images are analyzed and reconstructed based on principal component analysis (PCA) and then the differential ones between the reconstruction images with different numbers of PCA components can be generated. The shiny Eigen-fac...