Sparse principal component analysis (PCA) addresses the problem of finding a linear combination of the variables in a given data set with a sparse coefficients vector that maximizes the variability of the data. This model enhances the ability to interpret the principal components, and is applicable in a wide variety of fields including genetics and finance, just to name a few. We suggest a nece...

Principal component analysis (PCA), a well-established technique for data analysis and processing, provides a convenient form of dimensionality reduction that is effective for cleaning small Gaussian noises presented in the data. However, the applicability of standard principal component analysis in real scenarios is limited by its sensitivity to large errors. In this paper, we tackle the chall...

The robust estimation of the low-dimensional subspace that spans the data from a set of high-dimensional, possibly corrupted by gross errors and outliers observations is fundamental in many computer vision problems. The state-of-the-art robust principal component analysis (PCA) methods adopt convex relaxations of `0 quasi-norm-regularised rank minimisation problems. That is, the nuclear norm an...

this paper presents an integrated data envelopment analysis (dea) – principal component analysis (pca) – analytical hierarchy process (ahp) to achieve the efficiency scores and ranks of the insurance companies. fourteen insurance companies with thirteen input and output variables have been considered for the purpose of this study. since the dea model is sensitive to the number of variables in c...

Recently, fault detection and process monitoring using principal component analysis (PCA) were studied intensively and largely applied to industrial process. PCA is the optimal linear transformation with respect to minimizing the mean squared prediction error. If the data have nonlinear dependencies, an important issue is to develop a technique which can take into account this kind of dependenc...

Meat, as an important source of protein, is one of the main parts of many people’s diet. Due toeconomic interests and thereupon adulteration, there are special concerns on its accurate labeling.In this study Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrictechniques (principal component analysis (PCA), artificial neural networks (ANNs), and partial<...

Our contribution briefly outlines the basics of the well-established technique in data mining, namely the principal component analysis (PCA), and a rapidly emerging novel method, that is, the independent component analysis (ICA). The performance of PCA singular value decomposition-based and stationary linear ICA in blind separation of artificially generated data out of linear mixtures was criti...

Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are the techniques that deal with extracting the independent components from linear mixtures of Gaussian and non-Gaussian data at the input respectively. PCA is a classical method that deals with the second order statistics of data. It is also known as Karhunen-Loeve Transform or the Hotelling Transform in some applicat...

the analysis of cross-correlations is extensively applied for understanding of interconnections in stock markets. variety of methods are used in order to search stock cross-correlations including the random matrix theory (rmt), the principal component analysis (pca) and the hierachical ‎structures.‎ in ‎this work‎, we analyze cross-crrelations between price fluctuations of 20 ‎company ‎stocks‎‎...