Structure Constrained Discriminative Non-negative Matrix Factorization for Feature Extraction

Intelligent Computing Methodologies - Lecture Notes in Computer Science ◽

10.1007/978-3-319-09339-0_65 ◽

2014 ◽

pp. 645-657

Author(s):

Yan Jin ◽

Lisi Wei ◽

Yugen Yi ◽

Jianzhong Wang

Keyword(s):

Feature Extraction ◽

Matrix Factorization ◽

Non Negative Matrix Factorization

Download Full-text

Non-negative matrix factorization-based time-frequency feature extraction of voice signal for Parkinson's disease prediction

Computer Speech & Language ◽

10.1016/j.csl.2021.101216 ◽

2021 ◽

pp. 101216

Author(s):

Biswajit Karan ◽

Sitanshu Sekhar Sahu ◽

Juan Rafael Orozco-Arroyave ◽

Kartik Mahto

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Feature Extraction ◽

Matrix Factorization ◽

Disease Prediction ◽

Time Frequency ◽

Voice Signal ◽

Frequency Feature ◽

Non Negative Matrix Factorization

Download Full-text

Solution space of non negative matrix factorization and consideration of feature extraction on web application attacks

2014 International Conference on Information Science, Electronics and Electrical Engineering ◽

10.1109/infoseee.2014.6947797 ◽

2014 ◽

Author(s):

Takeshi Matsuda

Keyword(s):

Feature Extraction ◽

Matrix Factorization ◽

Web Application ◽

Solution Space ◽

Non Negative Matrix Factorization

Download Full-text

A New Algorithm for Feature Extraction of Plant Diseases and Insect Pests Signal Based on Optimal Wavelet Packet and Non-negative Matrix Factorization

Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016) ◽

10.2991/icence-16.2016.91 ◽

2016 ◽

Author(s):

Changcheng Li ◽

Shuyun Cai ◽

Laiwu Yin

Keyword(s):

Feature Extraction ◽

Matrix Factorization ◽

Insect Pests ◽

Wavelet Packet ◽

Plant Diseases ◽

Non Negative Matrix Factorization

Download Full-text

Feature Extraction Based on the Non-Negative Matrix Factorization of Convolutional Neural Networks for Monitoring Domestic Activity With Acoustic Signals

IEEE Access ◽

10.1109/access.2020.3007199 ◽

2020 ◽

Vol 8 ◽

pp. 122384-122395

Author(s):

Seokjin Lee ◽

Hee-Suk Pang

Keyword(s):

Neural Networks ◽

Feature Extraction ◽

Convolutional Neural Networks ◽

Matrix Factorization ◽

Acoustic Signals ◽

Domestic Activity ◽

Non Negative Matrix Factorization

Download Full-text

Image fusion based on non-negative matrix factorization and infrared feature extraction

2013 6th International Congress on Image and Signal Processing (CISP) ◽

10.1109/cisp.2013.6745210 ◽

2013 ◽

Cited By ~ 2

Author(s):

Jiao Mou ◽

Wei Gao ◽

Zongxi Song

Keyword(s):

Feature Extraction ◽

Image Fusion ◽

Matrix Factorization ◽

Non Negative Matrix Factorization

Download Full-text

Feature extraction via multi-view non-negative matrix factorization with local graph regularization

2015 IEEE International Conference on Image Processing (ICIP) ◽

10.1109/icip.2015.7351455 ◽

2015 ◽

Cited By ~ 16

Author(s):

Zhenfan Wang ◽

Xiangwei Kong ◽

Haiyan Fu ◽

Ming Li ◽

Yujia Zhang

Keyword(s):

Feature Extraction ◽

Matrix Factorization ◽

Graph Regularization ◽

Local Graph ◽

Non Negative Matrix Factorization

Download Full-text

Using non-negative matrix factorization with projected gradient for hyperspectral images feature extraction

2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA) ◽

10.1109/iciea.2013.6566423 ◽

2013 ◽

Cited By ~ 1

Author(s):

Bai Lin ◽

Gao Tao ◽

Du Kai

Keyword(s):

Feature Extraction ◽

Matrix Factorization ◽

Hyperspectral Images ◽

Projected Gradient ◽

Non Negative Matrix Factorization

Download Full-text

Hierarchical feature extraction by multi-layer non-negative matrix factorization network for classification task

Neurocomputing ◽

10.1016/j.neucom.2014.08.095 ◽

2015 ◽

Vol 165 ◽

pp. 63-74 ◽

Cited By ~ 17

Author(s):

Hyun Ah Song ◽

Bo-Kyeong Kim ◽

Thanh Luong Xuan ◽

Soo-Young Lee

Keyword(s):

Feature Extraction ◽

Matrix Factorization ◽

Classification Task ◽

Non Negative Matrix Factorization

Download Full-text

Label propagation based semi-supervised non-negative matrix factorization for feature extraction

Neurocomputing ◽

10.1016/j.neucom.2014.07.031 ◽

2015 ◽

Vol 149 ◽

pp. 1021-1037 ◽

Cited By ~ 20

Author(s):

Yugen Yi ◽

Yanjiao Shi ◽

Huijie Zhang ◽

Jianzhong Wang ◽

Jun Kong

Keyword(s):

Feature Extraction ◽

Matrix Factorization ◽

Label Propagation ◽

Non Negative Matrix Factorization

Download Full-text

Compressed Sensing Recognition Algorithm for Sonar Image Based on Non-Negative Matrix Factorization and Adjacency Spectra Feature

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.383-390.5818 ◽

2011 ◽

Vol 383-390 ◽

pp. 5818-5823

Author(s):

Yan Ling Hao ◽

Liang Wang

Keyword(s):

Feature Extraction ◽

Compressed Sensing ◽

Matrix Factorization ◽

Feature Vector ◽

Original Data ◽

Recognition Algorithm ◽

Identification Rate ◽

Sonar Image ◽

High Efficient ◽

Non Negative Matrix Factorization

A compressed sensing (CS) recognition algorithm for sonar image based on non-negative matrix factorization and adjacency spectra (A-NMF) feature extraction is proposed in this paper. The feature vector of the tradition CS recognition algorithm is random Gaussian matrix, which causes the identification rate unstable. The stable feature vector which is extracted by the A-NMF feature extraction is used in this algorithm. The feature and structure of the original data can be expressed more accurately by this feature vector. Then the sonar images are classified under the compressed sensing framework. Experimental results show that the sonar image recognition is high, efficient and stable.

Download Full-text