BACKGROUND Single Nucleotide Polymorphisms (SNPs) are one of the largest sources of new data in biology. In most papers, SNPs between individuals are visualized with Principal Component Analysis (PCA), an older method for this purpose. PRINCIPAL FINDINGS We compare PCA, an aging method for this purpose, with a newer method, t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualiza...

Principal-component analysis (PCA) has been used for decades to summarize the human genetic variation across geographic regions and to infer population migration history. Reduction of spurious associations due to population structure is crucial for the success of disease association studies. Recently, PCA has also become a popular method for detecting population structure and correction of popu...

This paper proposes a new glasses removal method from color frontal facial image to generate gray glassless facial image. The proposed method is based on recursive PCA reconstruction. For the generation of glassless images, the occluded region by glasses should be found, and a good reconstructed image to compensate with should be obtained. The recursive PCA reconstruction provides us with both ...

Linear–dendrite copolymers containing hyper branched poly(citric acid) and linear poly(ethylene glycol) blocks PCA–PEG–PCA are promising nonmaterial to use  in nanomedicine. To investigate their potential application in biological systems (especially for drug carries) ONIOM2 calculations were applied to study the nature of particular interactions between drug and the polymeric nanoparticle...

A new method is developed for analyzing the time-varying spectral content of EEG data collected in cognitive tasks. The goal is to extract and summarize the most salient features of numerical results, which span space, time, frequency, task conditions, and multiple subjects. Direct generalization of an established approach for analyzing event-related potentials, which uses sequential PCA follow...

Principal Component Analysis (PCA) plus Linear Discriminant Analysis (LDA) (PCA+LDA) and LDA/QR are both two-stage methods that deal with the small sample size (SSS) problem in traditional LDA. When applied to face recognition under varying lighting conditions and different facial expressions, neither method may work robustly due to limited number of training samples for each class in the train...

BACKGROUND/AIMS Association studies using unrelated individuals have become the most popular design for mapping complex traits. One of the major challenges of association mapping is avoiding spurious association due to population stratification. Principal component analysis (PCA) on genome-wide marker genotypes is one of the most popular population stratification control methods. It implicitly ...