Robust Principal Component Analysis Based on Fuzzy Local Information Reservation.

Principal Component Analysis (PCA) aims to acquire the principal component space containing the essential structure of data, instead of being used for mining and extracting the essential structure of data. In other words, the principal component space contains not only information related to the essential structure of data but also some unrelated information. This frequently occurs when the intrinsic dimensionality of data is unknown or when it has complex distribution characteristics such as multi-modalities, manifolds, etc.

Cyclophosphamide induced early remission and was superior to rituximab in idiopathic membranous nephropathy patients with high anti-PLA2R antibody levels.

Rituximab (RTX) and cyclophosphamide (CYC) based treatments are both recommended as first-line therapies in idiopathic membranous nephropathy (IMN) by KDIGO 2021 guideline. However, the efficacy of RTX vs. CYC-based treatments in IMN is still controversial.

Thermally Activated Microstructural Evolution of PtIrCu Alloyed Nanorings: Insights from Molecular Dynamics Simulations.

Nanoalloys have attracted extensive interest from the research and industrial community due to their unique properties. In this work, the thermally activated microstructural evolution and resultant collapse of PtIrCu nanorings were investigated using molecular dynamics simulations. Three PtIrCu nanorings with a fixed outer radius and varied inner radii were addressed to investigate the size effects on their thermal and shape stabilities.

Fuzzy Sparse Deviation Regularized Robust Principal Component Analysis.

Robust principal component analysis (RPCA) is a technique that aims to make principal component analysis (PCA) robust to noise samples. The current modeling approaches of RPCA were proposed by analyzing the prior distribution of the reconstruction error terms. However, these methods ignore the influence of samples with large reconstruction errors, as well as the valid information of these samples in principal component space, which will degrade the ability of PCA to extract the principal component of data.