The singular value decomposition SVD is a powerful technique in many matrix computa tions and analyses Using the SVD of a matrix in computations rather than the original matrix has the advantage of being more robust to numerical error Additionally the SVD exposes the geometric structure of a matrix an important aspect of many matrix calcula tions A matrix can be described as a tranformation fro...

Recent studies showed a link between cerebral small vessel white matter disease (SVD) and dizziness: patients whose dizziness cannot be explained by vestibular disease show severe SVD and gait abnormalities; however, little is still known about how SVD can cause this symptom. The primary aim of this study is to examine the possible underlying causes of dizziness in neurovascular patients; this ...

Singular Value Decomposition (SVD) is a useful tool in Functional Data Analysis (FDA). Compared to Principal Component Analysis (PCA), SVD is more fundamental, because SVD simultaneously provides the PCAs in both row and column spaces. We compare SVD and PCA from the FDA view point, and extend the usual SVD to variations by considering different centerings. A generalized scree plot is proposed ...

BACKGROUND Structural valve deterioration (SVD) is a major flaw of bioprostheses. Early SVD has been suspected in the last models of Mitroflow bioprosthesis. We sought to assess the incidence, mode, and impact of SVD on outcome in a large series of Mitroflow aortic valve replacement. METHODS AND RESULTS Six hundred seventeen consecutive patients (aged 76.1±6.3 years) underwent aortic valve re...

Singular Value Decomposition (SVD) has recently emerged as a new paradigm for processing different types of images. SVD is an attractive algebraic transform for image processing applications. The paper proposes an experimental survey for the SVD as an efficient transform in image processing applications. Despite the well-known fact that SVD offers attractive properties in imaging, the exploring...

Aiming at the high time complexity and poor accuracy of traditional SVD in hyperspectral recognition. we proposed F-SVD, which introduces latent factors(F) into decomposition strategy uses correlation between variable original to improve singular matrix. Firstly, used F-SVD reduce dimension visible-near infrared image, consequently designed a forage recognition model based on XGBoost. When test...

BACKGROUND AND PURPOSE The underlying mechanisms of small vessel disease (SVD) subtypes are diffuse arteriopathy (diffuse-SVD) or microatheroma (focal-SVD). Endothelial dysfunction by beta-amyloid peptide (Abeta) deposition has been associated with lacunar infarcts and leukoaraiosis, but its specific relationship with SVD subtypes is unknown. We hypothesized that plasma Abeta levels can play a ...

Background and Purpose—The underlying mechanisms of small vessel disease (SVD) subtypes are diffuse arteriopathy (diffuse-SVD) or microatheroma (focal-SVD). Endothelial dysfunction by -amyloid peptide (A ) deposition has been associated with lacunar infarcts and leukoaraiosis, but its specific relationship with SVD subtypes is unknown. We hypothesized that plasma A levels can play a different r...