Results 61 to 70 of about 1,958,095 (294)
Performance Guaranteesfor Schatten-p Quasi-Norm Minimization in Recovery of Low-Rank Matrices
, 2015 We address some theoretical guarantees for Schatten-p quasi-norm minimization (p∈(0,1]p∈(0,1]) in recovering low-rank matrices from compressed linear measurements.
Babaie-Zadeh, Massoud +2 more
core +1 more source
Tumour‐proximal deep serum proteomics identifies biomarkers associated with ovarian cancer
Molecular Oncology, EarlyView.New biomarkers are needed for early ovarian cancer detection. Ovarian (tumour‐proximal) and peripheral vein serum from healthy women and patients with benign or malignant tumours underwent nanoparticle‐enriched deep proteomics. Ovarian serum yielded more protein identifications and revealed tumour‐reflective signatures.
Darren Lau +9 more
wiley +1 more source
Alternating Direction Method of Multipliers for Generalized Low-Rank Tensor Recovery
Algorithms, 2016 Low-Rank Tensor Recovery (LRTR), the higher order generalization of Low-Rank Matrix Recovery (LRMR), is especially suitable for analyzing multi-linear data with gross corruptions, outliers and missing values, and it attracts broad attention in the fields
Jiarong Shi +3 more
doaj +1 more source
FEBS Open Bio, EarlyView.RoboMic is an automated confocal microscopy pipeline for high‐throughput functional imaging in living cells. Demonstrated with fluorescence recovery after photobleaching (FRAP), it integrates AI‐driven nuclear segmentation, ROI selection, bleaching, and analysis.
Selçuk Yavuz +6 more
wiley +1 more source
Numerical Experiments for Nuclear Norm Minimization Problems
, 2010 Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry.
김남희
core +1 more source
Fast Algorithms for Solving H-infinity-Norm Minimization Problems [PDF]
, 2001 In this paper we propose an efficient computation approach to minimize the H-infinity-norm of a transfer-function matrix depending affinely on a set of free parameters.
Varga, Andras, Parrilo, Pablo
core
A Fast and Accurate Matrix Completion Method based on QR Decomposition and L 2,1-Norm Minimization
, 2019 International audienceLow-rank matrix completion aims to recover matrices with missing entries and has attracted considerable attention from machine learning researchers.
Han, Fei +5 more
core +1 more source
Data Selection for Support Vector Machine Classifiers [PDF]
, 2000 The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classi er, is formulated as a concave minimization problem and solved by a nite number of linear programs.
Olvi L. Mangasarian +3 more
core +1 more source
RoundMi: A quantitative method to analyze mitochondrial morphology in mitotic cells
FEBS Open Bio, EarlyView.RoundMi is a workflow for rapid analysis of mitochondrial morphology in mitotic cells. By combining adaptive preprocessing with automated segmentation and quantification, it enables accurate measurements from single focal plane images, reducing acquisition time and computational demands while remaining compatible with high‐throughput fixed and live ...
Elmira Parvindokht Bararpour +2 more
wiley +1 more source
Enhancing Sparsity in Linear Prediction of Speech by Iteratively Reweighted 1-norm Minimization [PDF]
, 2010 Linear prediction of speech based on 1-norm minimization has already proved to be an interesting alternative to 2-norm minimization. In particular, choosing the 1-norm as a convex relaxation of the 0-norm, the corresponding linear prediction model offers
Giacobello, Daniele +11 more
core +1 more source