Abstract The principal component analysis (PCA) method and the singular value decomposition (SVD) are widely used for foreground subtraction in 21 cm intensity mapping experiments. We show their equivalence, point out that condition completely clean separation of foregrounds cosmic signal using PCA/SVD is unrealistic. propose a PCA-based method, dubbed “singular vector projection (SVP)” which e...

Abstract In order to effectively detect the waveform characteristics of closing inrush current distribution transformers and distinguish between magnetizing fault when closing, this paper proposes a new method based on neighborhood preserving embedding (NPE) principal component analysis (PCA) transformer detection method. This can process global local feature information data. First, NPE-PCA al...

For the improvement of reliability, safety and efficiency advanced methods of supervision, fault detection and fault diagnosis become increasingly important for many technical processes. This holds especially for safety related processes like aircraft, trains, automobiles, power plants and chemical plants. The fault detection based upon multivariate statistical projection method such as Princip...

Principal Component Analysis (PCA) has been applied for the feature extraction of high-dimensional data in pattern recognition. However, PCA does can not extract nonlinear characteristics of the datadistribution appropriately. In order to solve this problem, we have proposed a method of nonlinear PCA (NLPCA) which preserves the order of the principal components and we have implemented the NLPCA...

A robust method for dealing with the gross errors in the data collected for PCA model is proposed. This method, using M-estimator based on the generalized t distribution, adaptively transforms the data in the score space in order to eliminate the effects of the outliers in the original data. The robust estimation of the covariance or correlation matrix is obtained by the proposed approach so th...

Spatial dimension reduction called Two Dimensional PCA method has recently been presented. The application of this variation of traditional PCA considers images as 2D matrices instead of 1D vectors as other dimension reduction methods have been using. The application of these advances to verification techniques, using SVM as classification algorithm, is here shown. The simulation has been perfo...

In this paper, we address the problem of detecting outlier samples with highly different expression patterns in microarray data. Although outliers are not common, they appear even in widely used benchmark data sets and can negatively affect microarray data analysis. It is important to identify outliers in order to explore underlying experimental or biological problems and remove erroneous data....