Application of Nonnegative Tucker Decomposition in Medical Data Analysis

2014 ◽  
Vol 971-973 ◽  
pp. 1874-1883
Author(s):  
Akio Ishida ◽  
Kei Fujii ◽  
Naoki Yamamoto ◽  
Jun Murakami ◽  
Satoko Saito ◽  
...  
Keyword(s):  
Data Analysis ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Functional Independence Measure ◽  
Principal Component ◽  
Functional Independence ◽  
Medical Data ◽  
Tucker Decomposition ◽  
Higher Dimensional

In recent years, we have been studied about medical data analysis, especially for the rehabilitation data provided by a hospital, and extracted the recovery tendency of patients from their Functional Independence Measure (FIM) data. This time, we adopt the nonnegative Tucker decomposition (NTD) method, which is known as an extension of the nonnegative matrix factorization (NMF) to higher-dimensional data, to the medical data built up by piling each FIM data at some time points for several patients. Since the all elements of the tensor and matrices obtained by the NTD are nonnegative, it is expected that this method makes the interpretation of the characteristic vectors which are obtained from the resulting matrices easy and intelligible in comparison with our former approach, which used the multi-dimensional principal component analysis (MPCA). The experimental results show the effectiveness of proposed approach.

Measurement and Analysis of the Functional Independence Measure Data by Using Nonnegative Matrix Factorization Method

2013 ◽  
Vol 718-720 ◽  
pp. 630-635 ◽  
Author(s):  
Naoki Yamamoto ◽  
Jun Murakami ◽  
Kei Fujii ◽  
Chiharu Okuma ◽  
Satoko Saito ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Functional Independence Measure ◽  
Factorization Method ◽  
Functional Independence ◽  
Medical Data ◽  
Experimental Results ◽  
Measure Data ◽  
Measurement And Analysis

In this paper, we describe about a manner of adapting the nonnegative matrix factorization (NMF) method to the medical data, especially functional independence measure (FIM) data, and its experimental results. From the results which were obtained by applying the method to actually measured medical data in a hospital, we confirmed that the NMF method was effective to analyze the patients' characteristics related to disability and recovery tendency.

scholarly journals MHSNMF: multi-view hessian regularization based symmetric nonnegative matrix factorization for microbiome data analysis

2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Yuanyuan Ma ◽  
Junmin Zhao ◽  
Yingjun Ma
Keyword(s):  
Data Analysis ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Rapid Development ◽  
Nonnegative Matrix ◽  
Metabolomics Data ◽  
Normalized Mutual Information ◽  
Microbiome Data ◽  
Symmetric Nonnegative Matrix Factorization ◽  
Microbiome Data Analysis

Abstract Background With the rapid development of high-throughput technique, multiple heterogeneous omics data have been accumulated vastly (e.g., genomics, proteomics and metabolomics data). Integrating information from multiple sources or views is challenging to obtain a profound insight into the complicated relations among micro-organisms, nutrients and host environment. In this paper we propose a multi-view Hessian regularization based symmetric nonnegative matrix factorization algorithm (MHSNMF) for clustering heterogeneous microbiome data. Compared with many existing approaches, the advantages of MHSNMF lie in: (1) MHSNMF combines multiple Hessian regularization to leverage the high-order information from the same cohort of instances with multiple representations; (2) MHSNMF utilities the advantages of SNMF and naturally handles the complex relationship among microbiome samples; (3) uses the consensus matrix obtained by MHSNMF, we also design a novel approach to predict the classification of new microbiome samples. Results We conduct extensive experiments on two real-word datasets (Three-source dataset and Human Microbiome Plan dataset), the experimental results show that the proposed MHSNMF algorithm outperforms other baseline and state-of-the-art methods. Compared with other methods, MHSNMF achieves the best performance (accuracy: 95.28%, normalized mutual information: 91.79%) on microbiome data. It suggests the potential application of MHSNMF in microbiome data analysis. Conclusions Results show that the proposed MHSNMF algorithm can effectively combine the phylogenetic, transporter, and metabolic profiles into a unified paradigm to analyze the relationships among different microbiome samples. Furthermore, the proposed prediction method based on MHSNMF has been shown to be effective in judging the types of new microbiome samples.

scholarly journals Diffuse Optical Imaging Using Decomposition Methods

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Binlin Wu ◽  
M. Alrubaiee ◽  
W. Cai ◽  
M. Xu ◽  
S. K. Gayen
Keyword(s):  
Experimental Data ◽  
Optical Imaging ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Principal Component ◽  
Matrix Decomposition ◽  
Decomposition Methods ◽  
Component Analysis ◽  
Diffuse Optical Imaging

Diffuse optical imaging (DOI) for detecting and locating targets in a highly scattering turbid medium is treated as a blind source separation (BSS) problem. Three matrix decomposition methods, independent component analysis (ICA), principal component analysis (PCA), and nonnegative matrix factorization (NMF) were used to study the DOI problem. The efficacy of resulting approaches was evaluated and compared using simulated and experimental data. Samples used in the experiments included Intralipid-10% or Intralipid-20% suspension in water as the medium with absorptive or scattering targets embedded.

scholarly journals Multi-layered Nonnegative Matrix Factorization Based on PCA for the Foreign Object Detection in Electricity Meters

2018 ◽  
Vol 246 ◽  
pp. 03008
Author(s):  
Zhang Jin ◽  
Ou Xi Yang ◽  
Wu Jian ◽  
Ou Xi ◽  
Zhou You ◽  
...  
Keyword(s):  
Object Detection ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Principal Component ◽  
Foreign Object ◽  
Identification Rate ◽  
Noise Interference ◽  
Source Signal ◽  
Sound Separation

Foreign object detection is an important part of quality control of electricity meters. An automatic detection device is developed based on acoustic identification. In order to suppress background noise interference, we design a novel sound separation algorithm to separate the mixed sound signals to obtain the target source signal produced by foreign objects. Firstly, the improved principal-component-analysis-based multi-layered nonnegative matrix factorization (PMNMF) is used to separate sound signals. Secondly, the SVM is used to classify and identify sound signals. A suppot vector machine (SVM) as the classifier is used to compare the PMNMF algorithm with the basic NMF algorithm. The results indicate that the sound data pre-processed with the improved NMF algorithm results in a significantly higher identification rate up to about 95%.

scholarly journals Accelerated Multiplicative Updates and Hierarchical ALS Algorithms for Nonnegative Matrix Factorization

2012 ◽  
Vol 24 (4) ◽  
pp. 1085-1105 ◽  
Author(s):  
Nicolas Gillis ◽  
François Glineur
Keyword(s):  
Data Analysis ◽  
Least Squares ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Computational Cost ◽  
Nonnegative Matrix ◽  
Careful Analysis ◽  
Hyperspectral Data ◽  
Projected Gradient Method ◽  
Multiplicative Updates

Nonnegative matrix factorization (NMF) is a data analysis technique used in a great variety of applications such as text mining, image processing, hyperspectral data analysis, computational biology, and clustering. In this letter, we consider two well-known algorithms designed to solve NMF problems: the multiplicative updates of Lee and Seung and the hierarchical alternating least squares of Cichocki et al. We propose a simple way to significantly accelerate these schemes, based on a careful analysis of the computational cost needed at each iteration, while preserving their convergence properties. This acceleration technique can also be applied to other algorithms, which we illustrate on the projected gradient method of Lin. The efficiency of the accelerated algorithms is empirically demonstrated on image and text data sets and compares favorably with a state-of-the-art alternating nonnegative least squares algorithm.

scholarly journals On identifying kinematic and muscle synergies: a comparison of matrix factorization methods using experimental data from the healthy population

2017 ◽  
Vol 117 (1) ◽  
pp. 290-302 ◽  
Author(s):  
Navid Lambert-Shirzad ◽  
H. F. Machiel Van der Loos
Keyword(s):  
Principal Component Analysis ◽  
Matrix Factorization ◽  
Muscle Activation ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Motor Synergies ◽  
Factorization Methods

Human motor behavior is highly goal directed, requiring the central nervous system to coordinate different aspects of motion generation to achieve the motion goals. The concept of motor synergies provides an approach to quantify the covariation of joint motions and of muscle activations, i.e., elemental variables, during a task. To analyze goal-directed movements, factorization methods can be used to reduce the high dimensionality of these variables while accounting for much of the variance in large data sets. Three factorization methods considered in this paper are principal component analysis (PCA), nonnegative matrix factorization (NNMF), and independent component analysis (ICA). Bilateral human reaching data sets are used to compare the methods, and advantages of each are presented and discussed. PCA and NNMF had a comparable performance on both EMG and joint motion data and both outperformed ICA. However, NNMF's nonnegativity condition for activation of basis vectors is a useful attribute in identifying physiologically meaningful synergies, making it a more appealing method for future studies. A simulated data set is introduced to clarify the approaches and interpretation of the synergy structures returned by the three factorization methods.NEW & NOTEWORTHY Literature on comparing factorization methods in identifying motor synergies using numerically generated, simulation, and muscle activation data from animal studies already exists. We present an empirical evaluation of the performance of three of these methods on muscle activation and joint angles data from human reaching motion: principal component analysis, nonnegative matrix factorization, and independent component analysis. Using numerical simulation, we also studied the meaning and differences in the synergy structures returned by each method. The results can be used to unify approaches in identifying and interpreting motor synergies.

Sign in / Sign up

Export Citation Format

Share Document