Principal Component Analysis (PCA) aims to learn compact and informative representations for data and has wide applications in machine learning, text mining and computer vision. Classical PCA based on a Gaussian noise model is fragile to noise of large magnitude. Laplace noise assumption based PCA methods cannot deal with dense noise effectively. In this paper, we propose Cauchy Principal Compo...

The selection of appropriate wavelets is an important target for any application. In this paper Face recognition has been performed using Principal component analysis (PCA), Gaussian based PCA and Gabor based PCA. PCA extracts the relevant information from complex data sets and provides a solution to reduce dimensionality. PCA is based on Euclidean distance calculation which is minimized by app...

This paper presents a comparative analysis of a new unsupervised PCA-based technique for steel plates texture segmentation towards defect detection. The proposed scheme called Variance Based Component Analysis or VBCA employs PCA for feature extraction, applies a feature reduction algorithm based on variance of eigenpictures and classifies the pixels as defective and normal. While the classic P...

The widely used principal component analysis (PCA) is implemented in nonlinear by an auto-associative neural network. Compared to other nonlinear versions, such as kernel PCA, such a nonlinear PCA has explicit encoding and decoding processes, and the data can be transformed back to the original space. Its data compression performance is similar to that of PCA, but data analysis performance such...

The method of Principal Components Analysis (PCA) is widely used in statistical data analysis for engineering and the sciences. It is an effective tool for reducing the dimensionality of datasets while retaining majority of the data information. This paper explores the method of using PCA for spacecraft pose estimation for the purpose of proximity operations, and adapts a novel kernel based PCA...

For using process operational data to realize process monitoring, kinds of improved Principal Components Analysis (PCA) have been applied to cope with complex industrial processes. In this paper, a novel nonlinear wavelet packet PCA (NLWPPCA) method, which combines input training network with wavelet packet PCA, is proposed. Wavelet packet PCA integrates ability of PCA to de-correlate the varia...

Principal Component Analysis (PCA) is often used for reducing the dimensionality of input feature space. However, the eigenspace based on PCA is not always the best feature space for pattern recognition. In this paper, we use the feature space based on Independent Component Analysis (ICA) and show that the ICA representation is more effective than the PCA representation for human action recogni...

Principal Component Analysis (PCA) has been widely used for efficient representation of face images data in a low dimensional subspace. In this study, we use PCA to analyse different properties of faces, such as gender, ethnicity, age and identity. Using Linear Discriminant Analysis (LDA), we show that PCA efficiently encodes information related to different properties, different components of ...

In this study, new and feasible UV-visible spectrophotometric and multivariate spectrophotometric methods were described for the simultaneous determination of hydrochlorothiazide (HCTZ), hydralazine hydrochloride (H.HCl), and reserpine (RES) in combined pharmaceutical tablets. Methanol was used as a solvent for analysis and the whole UV region was scanned from 200-400 nm. The resolution was obt...