Results 81 to 90 of about 4,208,641 (395)

Principal Component Analysis in an Asymmetric Norm [PDF]

, 2014
Principal component analysis (PCA) is a widely used dimension reduction tool in the analysis of many kind of high-dimensional data. It is used in signal processing, mechanical engineering, psychometrics, and other fields under different names.
Haerdle, Wolfgang Karl, Osipenko, Maria, Tran, Ngoc Mai   +2 more
core   +3 more sources

Insights into the renal pathophysiology in Hermansky‐Pudlak syndrome‐1 from urinary extracellular vesicle proteomics and a new mouse model

FEBS Letters, EarlyView.
Hermansky‐Pudlak syndrome type 1 (HPS‐1) is a rare, autosomal recessive disorder with poorly understood renal involvement. Urinary extracellular vesicle (uEV) proteomics and a novel Hps1 mouse model reveal mitochondrial abnormalities and lipid accumulation in HPS‐1 kidney proximal tubule cells. Serum ApoA1 correlates with kidney function in our patient
Dawn M. Maynard, Bernadette R. Gochuico, Hadass Pri Chen, Christopher K. E. Bleck, Patricia M. Zerfas, Wendy J. Introne, William A. Gahl, May C. V. Malicdan   +7 more
wiley   +1 more source

Visualization of Iris Data Using Principal Component Analysis and Kernel Principal Component Analysis

Jurnal Ilmu Dasar, 2010
Principal component analysis (PCA) is a method used to reduce dimentionality of the dataset. However, the use of PCA failed to carry out the problem of non-linear and non-separable data.
Ismail Djakaria, suryo Guritno, Sri Haryatmi Kartiko   +2 more
doaj  

Principal Component Analysis on Recurrent Venous Thromboembolism

Clinical and Applied Thrombosis/Hemostasis, 2019
The rates of recurrent venous thromboembolism (RVTE) vary widely, and its causes still need to be elucidated. Statistical multivariate methods can be used to determine disease predictors and improve current methods for risk calculation.
Tiago D. Martins PhD, Joyce M. Annichino-Bizzacchi PhD, Anna V. C. Romano MSc, Rubens Maciel Filho PhD   +3 more
doaj   +1 more source

Probabilistic principal component analysis for metabolomic data

BMC Bioinformatics, 2010
Background Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is
Brennan Lorraine, Nyamundanda Gift, Gormley Isobel   +2 more
doaj   +1 more source

Exploring patterns enriched in a dataset with contrastive principal component analysis

Nature Communications, 2018
Visualization and exploration of high-dimensional data is a ubiquitous challenge across disciplines. Widely used techniques such as principal component analysis (PCA) aim to identify dominant trends in one dataset.
Abubakar Abid, Martin Jinye Zhang, V. Bagaria, James Y. Zou   +3 more
semanticscholar   +1 more source

Cytosolic‐enhanced dark Epac‐based FRET sensors allow for intracellular cAMP detection in live cells via FLIM

FEBS Letters, EarlyView.
We describe a novel set of Epac‐based FRET‐FLIM biosensors with improved fully cytosolic distribution, achieved without compromising the state‐of‐the‐art performance of our original designs, for detecting cAMP dynamics in real time in live cells with high precision and reliability.
Giulia Zanetti, Jeffrey B. Klarenbeek, Kees Jalink   +2 more
wiley   +1 more source

Iterated and exponentially weighted moving principal component analysis [PDF]

arXiv, 2021
The principal component analysis (PCA) is a staple statistical and unsupervised machine learning technique in finance. The application of PCA in a financial setting is associated with several technical difficulties, such as numerical instability and nonstationarity.
arxiv  

N-Dimensional Principal Component Analysis [PDF]

, 2010
In this paper, we first briefly introduce the multidimensional Principal Component Analysis (PCA) techniques, and then amend our previous N-dimensional PCA (ND-PCA) scheme by introducing multidirectional decomposition into ND-PCA implementation.
Yu, Hongchuan
core  

Fault Diagnosis Method Based on Principal Component Analysis and Broad Learning System

IEEE Access, 2019
Traditional feature extraction methods are used to extract the features of signal to construct the fault feature matrix, which exists the complex structure, higher correlation, and redundancy.
Huimin Zhao, Jianjie Zheng, Junjie Xu, Wu Deng   +3 more
semanticscholar   +1 more source