Results 51 to 60 of about 2,300,147 (337)
Integrated Principal Components Analysis
, 2018 Data integration, or the strategic analysis of multiple sources of data simultaneously, can often lead to discoveries that may be hidden in individualistic analyses of a single data source. We develop a new unsupervised data integration method named Integrated Principal Components Analysis (iPCA), which is a model-based generalization of PCA and serves
Tang, Tiffany M., Allen, Genevera I.
openaire +3 more sources
Psychosocial Outcomes in Patients With Endocrine Tumor Syndromes: A Systematic Review
Pediatric Blood &Cancer, EarlyView.ABSTRACT Introduction The combination of disease manifestations, the familial burden, and varying penetrance of endocrine tumor syndromes (ETSs) is unique. This review aimed to portray and summarize available data on psychosocial outcomes in patients with ETSs and explore gaps and opportunities for future research and care.
Daniël Zwerus +6 more
wiley +1 more source
Principal Component Analysis in ECG Signal Processing
EURASIP Journal on Advances in Signal Processing, 2007 This paper reviews the current status of principal component analysis in the area of ECG signal processing. The fundamentals of PCA are briefly described and the relationship between PCA and Karhunen-Loève transform is explained.
Roig José Millet +4 more
doaj +2 more sources
Simplicial Nonlinear Principal Component Analysis [PDF]
, 2012 We present a new manifold learning algorithm that takes a set of data points lying on or near a lower dimensional manifold as input, possibly with noise, and outputs a simplicial complex that fits the data and the manifold.
Hunt, Thomas, Krener, Arthur J.
core +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Purpose Metabolic syndrome (MetS) is a common complication in survivors of childhood acute lymphoblastic and myeloid leukemia (AL), and a major risk factor for premature cardiovascular disease, type‐2‐diabetes, and metabolic dysfunction‐associated steatotic liver disease (MASLD).
Visentin Sandrine +10 more
wiley +1 more source
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 +3 more
doaj +1 more source
Geometrical Approximated Principal Component Analysis for Hyperspectral Image Analysis
Remote Sensing, 2020 Principal Component Analysis (PCA) is a method based on statistics and linear algebra techniques, used in hyperspectral satellite imagery for data dimensionality reduction required in order to speed up and increase the performance of subsequent ...
Alina L. Machidon +4 more
doaj +1 more source
Multiscale principal component analysis
, 2013 Principal component analysis (PCA) is an important tool in exploring data. The conventional approach to PCA leads to a solution which favours the structures with large variances.
Akinduko, A. A., Gorban, A. N.
core +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background The Improving Population Outcomes for Renal Tumours of childhood (IMPORT) is a prospective clinical observational study capturing detailed demographic and outcome data on children and young people diagnosed with renal tumours in the United Kingdom and the Republic of Ireland.
Naomi Ssenyonga +56 more
wiley +1 more source
Low-Light Image Enhancement by Principal Component Analysis
IEEE Access, 2019 Under extreme low-lighting conditions, images have low contrast, low brightness, and high noise. In this paper, we propose a principal component analysis framework to enhance low-light-level images with decomposed luminance–chrominance components.
Steffi Agino Priyanka +2 more
doaj +1 more source