Component recognition with three-dimensional fluorescence spectra based on non-negative matrix factorization

2011 ◽  
Vol 25 (11) ◽  
pp. 586-591 ◽  
Author(s):  
Shaohui Yu ◽  
Yujun Zhang ◽  
Wenqing Liu ◽  
Nanjing Zhao ◽  
Xue Xiao ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Fluorescence Spectra ◽  
Three Dimensional ◽  
Component Recognition ◽  
Non Negative Matrix Factorization

Analysis on Three-Dimensional Fluorescence Spectra of PAHs in Soil Using Nonsmooth Non-Negative Matrix Factorization

2020 ◽  
Vol 47 (10) ◽  
pp. 1011002
Author(s):  
黄尧 Huang Yao ◽  
赵南京 Zhao Nanjing ◽  
孟德硕 Meng Deshuo ◽  
左兆陆 Zuo Zhaolu ◽  
程钊 Cheng Zhao ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Fluorescence Spectra ◽  
Three Dimensional ◽  
Non Negative Matrix Factorization

scholarly journals SAC-NMF-Driven Graphical Feature Analysis and Applications

2020 ◽  
Vol 2 (4) ◽  
pp. 630-646
Author(s):  
Nannan Li ◽  
Shengfa Wang ◽  
Haohao Li ◽  
Zhiyang Li
Keyword(s):  
Matrix Factorization ◽  
3D Model ◽  
Hidden Variables ◽  
Three Dimensional ◽  
Feature Space ◽  
Research Area ◽  
Feature Analysis ◽  
Push Forward ◽  
Fundamental Research ◽  
Non Negative Matrix Factorization

Feature analysis is a fundamental research area in computer graphics; meanwhile, meaningful and part-aware feature bases are always demanding. This paper proposes a framework for conducting feature analysis on a three-dimensional (3D) model by introducing modified Non-negative Matrix Factorization (NMF) model into the graphical feature space and push forward further applications. By analyzing and utilizing the intrinsic ideas behind NMF, we propose conducting the factorization on feature matrices constructed based on descriptors or graphs, which provides a simple but effective way to raise compressed and scale-aware descriptors. In order to enable part-aware model analysis, we modify the NMF model to be sparse and constrained regarding to both bases and encodings, which gives rise to Sparse and Constrained Non-negative Matrix Factorization (SAC-NMF). Subsequently, by adapting the analytical components (including hidden variables, bases, and encodings) to design descriptors, several applications have been easily but effectively realized. The extensive experimental results demonstrate that the proposed framework has many attractive advantages, such as being efficient, extendable, and so forth.

Integrated Similarity Indices for Component Recognition in Mixtures with Three-Dimensional Fluorescence Spectra

2011 ◽  
Vol 65 (4) ◽  
pp. 369-375 ◽  
Author(s):  
Shaohui Yu ◽  
Xue Xiao ◽  
Zhigang Wang ◽  
Kai Zhang ◽  
Gaofang Yin ◽  
...  
Keyword(s):  
Fluorescence Spectra ◽  
Three Dimensional ◽  
Similarity Indices ◽  
Component Recognition

Blind separation of fluorescence spectra using sparse non-negative matrix factorization on right hand factor

2015 ◽  
Vol 29 (8) ◽  
pp. 442-447 ◽  
Author(s):  
Ruifang Yang ◽  
Nanjing Zhao ◽  
Xue Xiao ◽  
Shaohui Yu ◽  
Jianguo Liu ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Fluorescence Spectra ◽  
Blind Separation ◽  
Right Hand ◽  
Non Negative Matrix Factorization

Hierarchical non-negative matrix factorization applied to three-dimensional 3 T MRSI data for automatic tissue characterization of the prostate

2016 ◽  
Vol 29 (6) ◽  
pp. 751-758 ◽  
Author(s):  
Teresa Laudadio ◽  
Anca R. Croitor Sava ◽  
Diana M. Sima ◽  
Alan J. Wright ◽  
Arend Heerschap ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Tissue Characterization ◽  
Three Dimensional ◽  
Mrsi Data ◽  
Non Negative Matrix Factorization

Approaches for Supporting Exploration for Analogical Inspiration With Behavior, Material and Component Based Structural Representations of Patent Databases

2018 ◽  
Author(s):  
Hyeonik Song ◽  
Katherine Fu
Keyword(s):  
Matrix Factorization ◽  
Mechanical Design ◽  
Three Dimensional ◽  
Graph Structure ◽  
Patent Data ◽  
Retrieval Process ◽  
Design By Analogy ◽  
Computational Methodology ◽  
Analogical Retrieval ◽  
Non Negative Matrix Factorization

This paper presents an explorative-based computational methodology to aid the analogical retrieval process in design-by-analogy practice. The computational methodology, driven by Non-negative Matrix Factorization (NMF), iteratively builds a hierarchical repositories of design solutions within which clusters of design analogies can be explored by designers. In the work, the methodology has been applied on a large repository of mechanical design related patents, processed to contain only component-, behavior-, or material-based content, to demonstrate that unique and valuable attribute-based analogical inspiration can be discovered from different representations of patent data. For explorative purposes, the hierarchical repositories have been visualized with a three-dimensional hierarchical structure and two-dimensional bar graph structure, which can be used interchangeably for retrieving analogies. This paper demonstrates that the explorative-based computational methodology provides designers an enhanced control over design repositories, empowering them to retrieve analogical inspiration for design-by-analogy practice.

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