Background Various clinical (disease activity, seropositive RA etc.) and metabolic risk factors (Dkk1 have been associated with erosive rheumatoid arthritis (RA). However, such might be intertwined, multicollinearity reduce our ability to discern the individual contribution score. Principal component analysis (PCA) is statistical technique for reducing dataset’s dimension principal regression (...

The goal of principal components analysis (PCA) is to find principal components in accordance with maximum variance of a data matrix. However, it has been shown recently that such variance-based principal components may not adequately represent image quality. As a result, a modified PCA approach based on maximization of SNR was proposed. Called maximum noise fraction (MNF) transformation or noi...

The paper presents an automatic classification system, which discriminates the different types of singlelayered clouds using Principal Component Analysis (PCA) with enhanced accuracy as compared to other techniques. PCA is an image classification technique typically used for face recognition. Principal components are the distinctive or peculiar features of an image. The approach described in th...

Kernel principal component analysis (kernel PCA) is a non-linear extension of PCA. This study introduces and investigates the use of kernel PCA for novelty detection. Training data are mapped into an infinite-dimensional feature space. In this space, kernel PCA extracts the principal components of the data distribution. The squared distance to the corresponding principal subspace is the measure...

The problem of ranking features computed by principal component analysis (PCA) in N-class problems have been addressed by the multi-class discriminant principal component analysis (MDPCA) and the Fisher discriminability criterion (FDC). These methods are motivated by the fact that PCA components do not necessarily represent important discriminant directions to separate sample groups. Given a da...

Principal component analysis (PCA) is one of the most widely used multivariate techniques in statistics. It is commonly used to reduce the dimensionality of data in order to examine its underlying structure and the covariance/correlation structure of a set of variables. While singular value decomposition provides a simple means for identification of the principal components (PCs) for classical ...

Sparse principal component analysis combines the idea of sparsity with principal component analysis (PCA). There are two kinds of sparse PCA; sparse loading PCA (slPCA) which keeps all the variables but zeroes out some of their loadings; and sparse variable PCA (svPCA) which removes whole variables by simultaneously zeroing out all the loadings on some variables. In this paper we propose a mode...

Neural gas network is a single-layered soft competitive neural network, which can be applied to clustering analysis with fast convergent speed comparing to Self-organizing Map (SOM), K-means etc. Combining neural gas with principal component analysis, this paper proposes a new clustering method, namely principal components analysis neural gas (PCA-NG), and the online learning algorithm is also ...

In analysis of bioinformatics data, a unique challenge arises from the high dimensionality of measurements. Without loss of generality, we use genomic study with gene expression measurements as a representative example but note that analysis techniques discussed in this article are also applicable to other types of bioinformatics studies. Principal component analysis (PCA) is a classic dimensio...

We provide a probabilistic and infinitesimal view of how the principal component analysis procedure (PCA) can be generalized to analysis of nonlinear manifold valued data. Starting with the probabilistic PCA interpretation of the Euclidean PCA procedure, we show how PCA can be generalized to manifolds in an intrinsic way that does not resort to linearization of the data space. The underlying pr...