ABSTRACT Dimensionality reduction is fundamental to applied spatial data analysis, condensing high‐dimensional indicators into parsimonious representations for mapping and modeling. Principal component analysis (PCA) remains a dominant approach, yet its linearity assumptions constrain its capacity to capture the complex, non‐linear dependencies ...
Alex Singleton +2 more
wiley +1 more source
A Novel Low-Rank Embedded Latent Multi-View Subspace Clustering Approach. [PDF]
Wang S, Chen L, Liang Z, Liu Q.
europepmc +1 more source
Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data. [PDF]
Shi Q, Hu B, Zeng T, Zhang C.
europepmc +1 more source
Multiphysics Simulation of Positive Streamer Discharge in Air Using Finite Element Method
ABSTRACT Streamer discharge modeling by means of the Finite Element Method (FEM) presents a formidable challenge in computational physics due to its nonlinear and convection‐dominated characteristics that cause numerical instabilities, including negative densities of charged particles.
Hasupama Jayasinghe +3 more
wiley +1 more source
scPEDSSC: proximity enhanced deep sparse subspace clustering method for scRNA-seq data. [PDF]
Wei X, Wu J, Li G, Liu J, Wu X, He C.
europepmc +1 more source
An Entropy Regularization k-Means Algorithm with a New Measure of between-Cluster Distance in Subspace Clustering. [PDF]
Xiong L, Wang C, Huang X, Zeng H.
europepmc +1 more source
Hybrid Coupling With Operator Inference and the Overlapping Schwarz Alternating Method
ABSTRACT This paper presents a hybrid approach for coupling subdomain‐local, nonintrusive Operator Inference (OpInf) reduced order models (ROMs) with each other and with subdomain‐local, high‐fidelity full order models (FOMs) using the overlapping Schwarz alternating method (O‐SAM).
Irina Tezaur +5 more
wiley +1 more source
Accelerated Stochastic Variance Reduction Gradient Algorithms for Robust Subspace Clustering. [PDF]
Liu H +5 more
europepmc +1 more source
Generalized gene co-expression analysis via subspace clustering using low-rank representation. [PDF]
Wang T, Zhang J, Huang K.
europepmc +1 more source
Multimodal Data‐Driven Microstructure Characterization
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source

