Results 71 to 80 of about 513,547 (285)
Convex sparse PCA for unsupervised feature learning
, 2016 © 2016 ACM. Principal component analysis (PCA) has been widely applied to dimensionality reduction and data preprocessing for different applications in engineering, biology, social science, and the like.
Chang, X +4 more
core +1 more source
Advanced Science, EarlyView.Prostate cancer is immunologically ‘cold’, with scarce, dysfunctional type 1 conventional dendritic cells (cDC1s) that limit T cell priming. We introduce an aptamer‐targeted liposomedelivering FMS‐like tyrosine kinase 3 ligand (Flt3L) and chlorin e6 (Ce6). Ultrasound induces antigen release and cDC1s recruitment, creating an in situ cDC1 vaccine.
Jiayi Wang +8 more
wiley +1 more source
Sparse outlier-robust PCA for multi-source data [PDF]
Sparse and outlier-robust Principal Component Analysis (PCA) has been a very active field of research recently. Yet, most existing methods apply PCA to a single dataset whereas multi-source data-i.e.
Filzmoser, Peter +2 more
core +5 more sources
Sparse PCA via Covariance Thresholding
J. Mach. Learn. Res., 2013 40 pages, 3 figures ...
Yash Deshpande, Andrea Montanari
openaire +4 more sources
Advanced Science, EarlyView.The pre‐regenerative vascular niche (PVN) is essential for nerve repair, yet its endothelial blueprint remains unclear. We identified angiogenic ECs (AECs) as the dominant pre‐regenerative subset and found that antler blood–derived exosomes (AB‐EXO) promote repair via IMP3.
Jinsheng Huang +11 more
wiley +1 more source
Fast Randomized PCA for Sparse Data
CoRR, 2018 Principal component analysis (PCA) is widely used for dimension reduction and embedding of real data in social network analysis, information retrieval, and natural language processing, etc. In this work we propose a fast randomized PCA algorithm for processing large sparse data.
Xu Feng +4 more
openaire +4 more sources
ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals
Advanced Science, EarlyView.The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray +3 more
wiley +1 more source
ArraySparse Principal Component Analysis (SPCA) is a widely used unsupervised model for genomic feature selection. However, the existing SPCA model still has significant deficiencies in long-pathway identification, biological interpretability, and model ...
Rui Miao +6 more
doaj +1 more source
Sparse HJ Biplot: A New Methodology via Elastic Net
Mathematics, 2021 The HJ biplot is a multivariate analysis technique that allows us to represent both individuals and variables in a space of reduced dimensions. To adapt this approach to massive datasets, it is necessary to implement new techniques that are capable of ...
Mitzi Cubilla-Montilla +3 more
doaj +1 more source
Sustainable Materials Design With Multi‐Modal Artificial Intelligence
Advanced Science, EarlyView.Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu +8 more
wiley +1 more source