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 aim of this study was to propose a method for improving the power of recognition and classification of thromboembolic syndrome based on the analysis of ‎ gene expression data using artificial neural networks. the studied method was performed on a dataset which contained data about 117 patients admitted to a hospital in durham in 2009. of all the studied patients, 66 patients were suffering ...

in this research work, the frequency domain (fd) of surface geochemical data was analyzed to decompose the complex geochemical patterns related to different depths of the mineral deposit. in order to predict the variation in mineralization in the depth and identify the deep geochemical anomalies and blind mineralization using the surface geochemical data for the dalli cu-au porphyry deposit, a ...

an ideal fusion method preserves the spectral information in fused image without spatial distortion. the pca is believed to be a well-known pan-sharpening approach and being widely used for its efficiency and high spatial resolution. however, it can distort the spectral characteristics of multispectral images. the current paper tries to present a new fusion method based on the same concept. in ...

Face recognition is a biometric analysis tool that has enabled surveillance systems to detect humans and recognize humans without their co-operation. In this paper we evaluate the basics of the Principal Component Analysis (PCA) and verify the results of this algorithm on a training database of images. The same principle is in effect used to recognise the gender of the test image by evaluating ...

We consider Fair Principal Component Analysis (FPCA) and search for a low dimensional subspace that spans multiple target vectors in fair manner. FPCA is defined as non-concave maximization of the worst projected norm within given set. The problem arises filter design signal processing, when incorporating fairness into dimensionality reduction schemes. state art approach to via semidefinite rel...