Individual observations can be very influential when performing classical Principal Component Analysis in a Euclidean space. Robust PCA algorithms detect and neutralize such dominating data points. This paper studies robustness issues for PCA in a kernel induced feature space. The sensitivity of Kernel PCA is characterized by calculating the influence function. A robust Kernel PCA method is pro...

The famous method Principal Components Analysis (PCA) is the basic approach for decomposition of 3D tensor images (for example, multiand hyper-spectral, multi-view, computer tomography, video, etc.). As a result of the processing, their information redundancy is significantly reduced. This is of high importance for the efficient compression and for the reduction of the features space needed, wh...

A spectral reflectance reconstruction method based on combining PCA and regularized polynomial is proposed to optimize the spectral data and channel response. Firstly, the PCA method is used to reduce the high dimensional spectral data of the training samples. Then, the polynomial expansion for channel response of the sample is carried out to improve the accuracy of spectral reconstruction thro...

Development in Human Computer Interactions (HCI) helps in budding user friendly systems to communicate with computers. One of the fundamental techniques that aid Human Computer Interaction (HCI) is face recognition. Face recognition is one of the most successful applications of image analysis and pattern recognition. Principle Component Analysis (PCA) is considered as the first real time face r...

Abstract. It is well known that Principal Component Analysis (PCA) is strongly affected by outliers and a lot of effort has been put into robustification of PCA. In this paper we present a new algorithm for robust PCA minimizing the trimmed reconstruction error. By directly minimizing over the Stiefel manifold, we avoid deflation as often used by projection pursuit methods. In distinction to ot...

Together with the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area since early 90. Nowadays, Principal Component Analysis (PCA) has been widely adopted as the most promising face recognition algorithm. Yet still, PCA has its limitations: poor discriminatory power and large computational lo...

We consider the problem of finding lower dimensional subspaces in the presence of outliers and noise in the online setting. In particular, we extend previous batch formulations of robust PCA to the stochastic setting with minimal storage requirements and runtime complexity. We introduce three novel stochastic approximation algorithms for robust PCA that are extensions of standard algorithms for...

We propose a novel method for functional segmentation of fMRI data that incorporates multiple functional attributes such as activation effects and functional connectivity, under a single framework. Similar to PCA, our method exploits the structure of the correlation matrix but with neighborhood information adaptively integrated to encourage detection of spatially contiguous clusters yet without...

Principal component analysis (PCA) is a popular dimension reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets. When the data distribution is skewed, data transformation is commonly used prior to applying PCA. Such transformation is usually obtained from previous studies, prior knowledge, or trial-and-error. In this work, we develop a model-b...

Recently, many l1-norm based PCA methods have been developed for dimensionality reduction, but they do not explicitly consider the reconstruction error. Moreover, they do not take into account the relationship between reconstruction error and variance of projected data. This reduces the robustness of algorithms. To handle this problem, a novel formulation for PCA, namely angle PCA, is proposed....