scholarly journals The Effect of ICA and Non-negative Matrix Factorization Analysis for EMG Signals Recorded From Multi-Channel EMG Sensors

2020 ◽  
Vol 14 ◽  
Author(s):  
Yeongdae Kim ◽  
Sorawit Stapornchaisit ◽  
Makoto Miyakoshi ◽  
Natsue Yoshimura ◽  
Yasuharu Koike
Keyword(s):  
Matrix Factorization ◽  
Decomposition Method ◽  
Index Finger ◽  
Power Source ◽  
Classification Performance ◽  
Emg Signal ◽  
Movement Artifacts ◽  
Low Dimensional ◽  
Sensor Positions ◽  
Non Negative Matrix Factorization

Surface electromyography (EMG) measurements are affected by various noises such as power source and movement artifacts and adjacent muscle activities. Hardware solutions have been found that use multi-channel EMG signal to attenuate noise signals related to sensor positions. However, studies addressing the overcoming of crosstalk from EMG and the division of overlaid superficial and deep muscles are scarce. In this study, two signal decompositions—independent component analysis and non-negative matrix factorization—were used to create a low-dimensional input signal that divides noise, surface muscles, and deep muscles and utilizes them for movement classification based on direction. In the case of index finger movement, it was confirmed that the proposed decomposition method improved the classification performance with the least input dimensions. These results suggest a new method to analyze more dexterous movements of the hand by separating superficial and deep muscles in the future using multi-channel EMG signals.

Non-Negative Matrix Factorization Based Uncertainty Quantification Method for Complex Networked Systems

2015 ◽  
Author(s):  
Arpan Mukherjee ◽  
Rahul Rai ◽  
Puneet Singla ◽  
Tarunraj Singh ◽  
Abani Patra
Keyword(s):  
Matrix Factorization ◽  
Decomposition Method ◽  
Uncertainty Propagation ◽  
Networked Systems ◽  
Computational Time ◽  
State Variables ◽  
System Decomposition ◽  
Cluster Interaction ◽  
Quantifying Uncertainty ◽  
Non Negative Matrix Factorization

The behavior of large networked systems with underlying complex nonlinear dynamic are hard to predict. With increasing number of states, the problem becomes even harder. Quantifying uncertainty in such systems by conventional methods requires high computational time and the accuracy obtained in estimating the state variables can also be low. This paper presents a novel computational Uncertainty Quantifying (UQ) method for complex networked systems. Our approach is to represent the complex systems as networks (graphs) whose nodes represent the dynamical units, and whose links stand for the interactions between them. First, we apply Non-negative Matrix Factorization (NMF) based decomposition method to partition the domain of the dynamical system into clusters, such that the inter-cluster interaction is minimized and the intra-cluster interaction is maximized. The decomposition method takes into account the dynamics of individual nodes to perform system decomposition. Initial validation results on two well-known dynamical systems have been performed. The validation results show that uncertainty propagation error quantified by RMS errors obtained through our algorithms are competitive or often better, compared to existing methods.

A Novel Discriminant Non-Negative Matrix Factorization and its Application to Facial Expression Recognition

2010 ◽  
Vol 143-144 ◽  
pp. 129-133
Author(s):  
Yan Li Zhu ◽  
Jun Chen ◽  
Pei Xin Qu
Keyword(s):  
Facial Expression ◽  
Matrix Factorization ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Discrimination Capability ◽  
Factorization Algorithm ◽  
Factorization Algorithms ◽  
Low Dimensional ◽  
Low Dimensional Features ◽  
Non Negative Matrix Factorization

The paper proposes a novel discriminant non-negative matrix factorization algorithm and applies it to facial expression recognition. Unlike traditional non-negative matrix factorization algorithms, the algorithm adds discriminant constraints in low-dimensional weights. The experiments on facial expression recognition indicate that the algorithm enhances the discrimination capability of low-dimensional features and achieves better performance than other non-negative matrix factorization algorithms.

Deep multiple non-negative matrix factorization for multi-view clustering

2021 ◽  
Vol 25 (2) ◽  
pp. 339-357
Author(s):  
Guowang Du ◽  
Lihua Zhou ◽  
Kevin Lü ◽  
Haiyan Ding
Keyword(s):  
Matrix Factorization ◽  
Superior Performance ◽  
Dimensional Representation ◽  
Geometric Information ◽  
Heterogeneous Information ◽  
Benchmark Datasets ◽  
Abstract Level ◽  
Low Dimensional ◽  
Shallow Structure ◽  
Non Negative Matrix Factorization

Multi-view clustering aims to group similar samples into the same clusters and dissimilar samples into different clusters by integrating heterogeneous information from multi-view data. Non-negative matrix factorization (NMF) has been widely applied to multi-view clustering owing to its interpretability. However, most NMF-based algorithms only factorize multi-view data based on the shallow structure, neglecting complex hierarchical and heterogeneous information in multi-view data. In this paper, we propose a deep multiple non-negative matrix factorization (DMNMF) framework based on AutoEncoder for multi-view clustering. DMNMF consists of multiple Encoder Components and Decoder Components with deep structures. Each pair of Encoder Component and Decoder Component are used to hierarchically factorize the input data from a view for capturing the hierarchical information, and all Encoder and Decoder Components are integrated into an abstract level to learn a common low-dimensional representation for combining the heterogeneous information across multi-view data. Furthermore, graph regularizers are also introduced to preserve the local geometric information of each view. To optimize the proposed framework, an iterative updating scheme is developed. Besides, the corresponding algorithm called MVC-DMNMF is also proposed and implemented. Extensive experiments on six benchmark datasets have been conducted, and the experimental results demonstrate the superior performance of our proposed MVC-DMNMF for multi-view clustering compared to other baseline algorithms.

scholarly journals Image Encryption Based on Matrix Factorization

2020 ◽  
Vol 10 (5) ◽  
pp. 655-661
Author(s):  
Vivek Khalane ◽  
Shekhar Suralkar ◽  
Umesh Bhadade
Keyword(s):  
Image Encryption ◽  
Matrix Factorization ◽  
Decomposition Method ◽  
Computation Time ◽  
Matrix Decomposition ◽  
Input Image ◽  
Decomposition Techniques ◽  
Encryption Method ◽  
State Of Art ◽  
Non Negative Matrix Factorization

In this paper, we present a matrix decomposition-based approach for image cryptography. The proposed method consists of decomposing the image into different component and scrambling the components to form the image encryption technique. We use two different type of matrix decomposition techniques to check the efficiency of proposed encryption method. The decomposition techniques used are Independent component analysis (ICA) and Non-Negative Matrix factorization (NMF). The proposed technique has unique user defined parameters (key) such as decomposition method, number of decomposition components and order in which the components are arranged. The unique encryption technique is designed on the basis of these key parameters. The original image can be reconstructed at the decryption end only if the selected parameters are known to the user. The design examples for both decomposition approaches are presented for illustration purpose. We analyze the complexity and encryption time of cryptography system. Results prove that the proposed scheme is more secure as it has less correlation between the input image and the encrypted version of the same as compared to state-of-art methods. The computation time of the proposed approach is found to be comparable.

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