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.

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

scholarly journals Feature Sampling Based Unsupervised Semantic Clustering for Real Web Multi-View Content

2019 ◽  
Vol 33 ◽  
pp. 102-109
Author(s):  
Xiaolong Gong ◽  
Linpeng Huang ◽  
Fuwei Wang
Keyword(s):  
Matrix Factorization ◽  
Clustering Algorithm ◽  
Iteration Process ◽  
Sampling Strategy ◽  
Matrix Decomposition ◽  
Feature Space ◽  
Graph Regularization ◽  
Semantic Clustering ◽  
Independence Criterion ◽  
Non Negative Matrix Factorization

Real web datasets are often associated with multiple views such as long and short commentaries, users preference and so on. However, with the rapid growth of user generated texts, each view of the dataset has a large feature space and leads to the computational challenge during matrix decomposition process. In this paper, we propose a novel multi-view clustering algorithm based on the non-negative matrix factorization that attempts to use feature sampling strategy in order to reduce the complexity during the iteration process. In particular, our method exploits unsupervised semantic information in the learning process to capture the intrinsic similarity through a graph regularization. Moreover, we use Hilbert Schmidt Independence Criterion (HSIC) to explore the unsupervised semantic diversity information among multi-view contents of one web item. The overall objective is to minimize the loss function of multi-view non-negative matrix factorization that combines with an intra-semantic similarity graph regularizer and an inter-semantic diversity term. Compared with some state-of-the-art methods, we demonstrate the effectiveness of our proposed method on a large real-world dataset Doucom and the other three smaller datasets.

A Survey: Object Feature Analysis Based on Non-negative Matrix Factorization

電腦學刊 ◽  
2021 ◽  
Vol 32 (6) ◽  
pp. 107-121
Author(s):  
Shuang Ma Shuang Ma ◽  
Jinhe Liu Shuang Ma ◽  
Liang Gao Jinhe Liu
Keyword(s):  
Matrix Factorization ◽  
Feature Analysis ◽  
Object Feature ◽  
Non Negative Matrix Factorization

scholarly journals Transferred Subspace Learning Based on Non-negative Matrix Factorization for EEG Signal Classification

2021 ◽  
Vol 15 ◽  
Author(s):  
Aimei Dong ◽  
Zhigang Li ◽  
Qiuyu Zheng
Keyword(s):  
Matrix Factorization ◽  
Feature Space ◽  
Signal Classification ◽  
Training Dataset ◽  
Eeg Signal ◽  
Practical Applications ◽  
Testing Dataset ◽  
Eeg Signal Classification ◽  
Original Feature ◽  
Non Negative Matrix Factorization

EEG signal classification has been a research hotspot recently. The combination of EEG signal classification with machine learning technology is very popular. Traditional machine leaning methods for EEG signal classification assume that the EEG signals are drawn from the same distribution. However, the assumption is not always satisfied with the practical applications. In practical applications, the training dataset and the testing dataset are from different but related domains. How to make best use of the training dataset knowledge to improve the testing dataset is critical for these circumstances. In this paper, a novel method combining the non-negative matrix factorization technology and the transfer learning (NMF-TL) is proposed for EEG signal classification. Specifically, the shared subspace is extracted from the testing dataset and training dataset using non-negative matrix factorization firstly and then the shared subspace and the original feature space are combined to obtain the final EEG signal classification results. On the one hand, the non-negative matrix factorization can assure to obtain essential information between the testing and the training dataset; on the other hand, the combination of shared subspace and the original feature space can fully use all the signals including the testing and the training dataset. Extensive experiments on Bonn EEG confirmed the effectiveness of the proposed method.

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

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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