Results 41 to 50 of about 1,708,415 (229)

A computational study of Ising‐based solvers for discrete landscape exploration in process optimization

AIChE Journal, EarlyView.
ABSTRACT Conceptual process design combines discrete configuration choices with continuous operating decisions, often yielding difficult mixed‐integer nonlinear or simulation‐based optimization problems. This work presents an exploratory computational assessment of Ising‐based solvers, simulated annealing, quantum annealing, and entropy computing, as ...
Yirang Park, David E. Bernal Neira
wiley   +1 more source

Deep Nonnegative Dictionary Factorization for Hyperspectral Unmixing

Remote Sensing, 2020
As a powerful blind source separation tool, Nonnegative Matrix Factorization (NMF) with effective regularizations has shown significant superiority in spectral unmixing of hyperspectral remote sensing images (HSIs) owing to its good physical ...
Wenhong Wang, Hongfu Liu
doaj   +1 more source

Hybridizing sparse component analysis with genetic algorithms for microarray analysis [PDF]

, 2008
Nonnegative matrix factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. However, it is known not to lead to unique results when applied to blind source separation (BSS) problems.
Stadlthanner, K., Puntonet, C. G., Stadlthanner, Kurt, Lang, Elmar, Lang, E. W., Puntonet, Carlos G., Górriz, J. M., Tomé, A. M., Theis, Fabian J., Theis, F. J.   +9 more
core   +1 more source

MetaPaCS: A Novel Meta‐Learning Framework for Pancreatic Cancer Subtype Identification

Advanced Intelligent Systems, EarlyView.
We have developed MetaPaCS, an ensemble meta‐learning framework for reliable prediction of pancreatic cancer subtypes with high accuracy. MetaPaCS is capable of capturing subtype‐specific distinct gene expression patterns and drug‐disease associations for pancreatic cancer.
Nick B. Peterson, Mengtao Sun, Xinchao Wu, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

Nonnegative matrix factorization requires irrationality [PDF]

, 2017
Nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative n × m matrix M into a product of a nonnegative n × d matrix W and a nonnegative d × m matrix H. A longstanding open question, posed by Cohen and Rothblum in 1993, is
Kiefer, Stefan, James Worrell, Worrell, James, Ines Marušić, Shirmohammadi, Mahsa, Dmitry Chistikov, Marušić, Ines, Mahsa Shirmohammadi, Stefan Kiefer, Chistikov, Dmitry   +9 more
core   +1 more source

Beyond the Camera: Epidermal Sensor Platforms for Intelligent Facial Expression Recognition and Interactive Human Monitoring

Advanced Intelligent Systems, EarlyView.
Skin‐conformal epidermal sensors enable camera‐free intelligent facial monitoring by converting facial deformation and muscle activity into electrical signals through piezoresistive, capacitive, electrophysiologic, and self‐powered mechanisms. Coupled with artificial intelligence (AI), these platforms support facial expression recognition, human ...
Jeehyun Joung, Manchi Nagaraju, Jun‐Gyu Choi, Jaeil Park, Sungjun Park, Daegun Kim   +5 more
wiley   +1 more source

Fairer non-negative matrix factorization

Frontiers in Big Data
There has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed alongside bias mitigation strategies that are practical
Lara Kassab, Erin George, Deanna Needell, Haowen Geng, Nika Jafar Nia, Aoxi Li   +5 more
doaj   +1 more source

Enhancing Hyperspectral Unmixing With Two-Stage Multiplicative Update Nonnegative Matrix Factorization

IEEE Access, 2019
Nonnegative matrix factorization (NMF) is a powerful tool for hyperspectral unmixing (HU). This method factorizes a hyperspectral cube into constituent endmembers and their fractional abundances.
Li Sun, Kang Zhao, Congying Han, Ziwen Liu   +3 more
doaj   +1 more source

Probabilistic Non-Negative Matrix Factorization with Binary Components

Mathematics, 2021
Non-negative matrix factorization is used to find a basic matrix and a weight matrix to approximate the non-negative matrix. It has proven to be a powerful low-rank decomposition technique for non-negative multivariate data.
Xindi Ma, Jie Gao, Xiaoyu Liu, Taiping Zhang, Yuanyan Tang   +4 more
doaj   +1 more source

Projective robust nonnegative factorization

, 2016
Nonnegative matrix factorization (NMF) has been successfully used in many fields as a low-dimensional representation method. Projective nonnegative matrix factorization (PNMF) is a variant of NMF that was proposed to learn a subspace for feature ...
You, Jane, Xu, Yong, Lu, Yuwu, Li, Xuelong, Lai, Zhihui, Yuan, Chun   +5 more
core   +1 more source