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...

By analyzing the direction characteristic of principal component analysis (PCA), we propose an edge detection method based on PCA. Using Karhunen–Loëve transform, PCA transforms the original dataset into lower-dimensional feature data. The transform has directivity both on energy accumulation and data selection. The author points out and proves the two direction characteristics. In this paper, ...

BACKGROUND It has been postulated that non-steroidal anti-inflammatory drugs (NSAIDs) use leads to decreased prostate cancer (PCa) risk. In recent years, NSAIDs' role in PCa development has been extensively studied; however, there is not yet a definitive answer. Moreover, the epidemiological results for NSAIDs' effect on PCa-specific mortality have been inconsistent. Therefore, we performed a m...

We study sparse principal components analysis in the high-dimensional setting, where p (the number of variables) can be much larger than n (the number of observations). We prove optimal, non-asymptotic lower and upper bounds on the minimax estimation error for the leading eigenvector when it belongs to an lq ball for q ∈ [0, 1]. Our bounds are sharp in p and n for all q ∈ [0, 1] over a wide cla...

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...

In face recognition, Principal Component Analysis (PCA) is often used to extract a low dimensional face representation based on the eigenvector of the face image autocorrelation matrix. Kernel Principal Component Analysis (Kernel PCA) has recently been proposed as a non-linear extension of PCA. While PCA is able to discover and represent linearly embedded manifolds, Kernel PCA can extract low d...

UNLABELLED Clinical significance of autoantibodies to parietal cells (PCA) in patients with autoimmune thyroid diseases (ATD) remains contradictory. AIM To characterize the frequency of PCA in patients with ATD; to clarify the role of gender and age in PCA positivity; to analyze the association of PCA with gastric or haematologic symptoms and with the levothyroxine dose required to achieve a ...

Prostate cancer (PCa) is the most common non-cutaneous malignancy in men and is responsible for the second most common age-adjusted death rate due to cancer among men in the US, after lung cancer [1]. The prevalence of PCa increases with age with the chance of detecting PCa in an 80 year-old male being as high as 60% [2]. While PCa is generally considered a disease of “old age” with most men dy...

AIMS To clarify the features of the background electroencephalographic (EEG) activities in clinically well preterm infants born at less than 27 weeks gestation and to outline their chronological changes with increasing postconceptional age (PCA). METHODS EEGs of clinically well premature infants born at less than 27 weeks gestation were recorded during the early postnatal period. The infants ...