In this paper we proposed an iterative elimination algorithm for sparse principal component analysis. It recursively eliminates variables according to certain criterion that aims to minimize the loss of explained variance, and reconsiders the sparse principal component analysis problem until the desired sparsity is achieved. Two criteria, the approximated minimal variance loss (AMVL) criterion ...

Principal component analysis (PCA) and its dual—principal coordinate analysis (PCO)—are widely applied to unsupervised dimensionality reduction. In this paper, we show that PCA and PCO can be carried out under regression frameworks. Thus, it is convenient to incorporate sparse techniques into the regression frameworks. In particular, we propose a sparse PCA model and a sparse PCO model. The for...

In analogy to the PCA setting, the sparse PCA problem is often solved by iteratively alternating between two subtasks: cardinality-constrained rank-one variance maximization and matrix deflation. While the former has received a great deal of attention in the literature, the latter is seldom analyzed and is typically borrowed without justification from the PCA context. In this work, we demonstra...

BACKGROUND/AIMS Patients being treated for prostate cancer (PCa) have an increased risk of developing colorectal cancer. However, whether PCa patients are inherently at a higher risk of colorectal neoplasms (CRNs) is unknown. We aimed to investigate the risk of CRNs in PCa patients. MATERIALS AND METHODS Patients who had been diagnosed with PCa at a tertiary medical center and had colonoscopy...

Prostate cancer (PCa) is the most frequently diagnosed type of cancer in Chinese males. Cell-cycle aberration is a hallmark of cancer. Spindle pole body component 25 homolog (SPC25), a component of the Ndc80 complex, serves an important role in regulating mitotic chromosome segregation. However, the functional roles of SPC25 in PCa remain poorly understood. To the best of our knowledge, the pre...

We propose shiny analysis framework accompanied with makeup deterioration using normalized facial images (MaVIC and the corresponding makeup-deteriorated data sets). These images are analyzed and reconstructed based on principal component analysis (PCA) and then the differential ones between the reconstruction images with different numbers of PCA components can be generated. The shiny Eigen-fac...

In this paper, we present a simple and straightforward primal-dual support vector machine formulation to the problem of principal component analysis (PCA) in dual variables. By considering a mapping to a high-dimensional feature space and application of the kernel trick (Mercer theorem), kernel PCA is obtained as introduced by Scholkopf et al. (2002). While least squares support vector machine ...

In this paper, an improved version of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) is proposed for feature extraction to classify the ischemic beats from electrocardiogram (ECG) signal. The Fuzzy C-Means (FCM) and Genetic Algorithm (GA) is combined with PCA and ICA to extract more relevant features; the proposed methods are named as Fuzzy-Genetic based PCA (FGPCA)...

Independent components analysis (ICA) and principal components analysis (PCA) are methods used to analyze event-related potential (ERP) and functional imaging (fMRI) data. In the present study, ICA and PCA were directly compared by applying them to simulated ERP datasets. Specifically, PCA was used to generate a subspace of the dataset followed by the application of PCA Promax or ICA Infomax ro...

miRNAs are a class of non-coding RNAs that exert critical roles in various biological processes. The aim of the present study was to identify the functional roles of miR-802 in regulating epithelial-mesenchymal transition (EMT) in prostate cancer (PCa). miR-802 expression was detected in 73 pairs of PCa samples and PCa cell lines (PC3 and DU145 cells) by qRT-PCR. Cell proliferation was detected...