scholarly journals Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1661
Author(s):  
Juan De La Torre Cruz ◽  
Francisco Jesús Cañadas Quesada ◽  
Nicolás Ruiz Reyes ◽  
Sebastián García Galán ◽  
Julio José Carabias Orti ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
State Of The Art ◽  
Point Of View ◽  
Biomedical Signal Processing ◽  
Low Rank ◽  
Respiratory Sounds ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Key Indicator ◽  
Non Negative Matrix Factorization

The appearance of wheezing sounds is widely considered by physicians as a key indicator to detect early pulmonary disorders or even the severity associated with respiratory diseases, as occurs in the case of asthma and chronic obstructive pulmonary disease. From a physician’s point of view, monophonic and polyphonic wheezing classification is still a challenging topic in biomedical signal processing since both types of wheezes are sinusoidal in nature. Unlike most of the classification algorithms in which interference caused by normal respiratory sounds is not addressed in depth, our first contribution proposes a novel Constrained Low-Rank Non-negative Matrix Factorization (CL-RNMF) approach, never applied to classification of wheezing as far as the authors’ knowledge, which incorporates several constraints (sparseness and smoothness) and a low-rank configuration to extract the wheezing spectral content, minimizing the acoustic interference from normal respiratory sounds. The second contribution automatically analyzes the harmonic structure of the energy distribution associated with the estimated wheezing spectrogram to classify the type of wheezing. Experimental results report that: (i) the proposed method outperforms the most recent and relevant state-of-the-art wheezing classification method by approximately 8% in accuracy; (ii) unlike state-of-the-art methods based on classifiers, the proposed method uses an unsupervised approach that does not require any training.

A constrained tonal semi-supervised non-negative matrix factorization to classify presence/absence of wheezing in respiratory sounds

2020 ◽  
Vol 161 ◽  
pp. 107188 ◽  
Author(s):  
J. Torre-Cruz ◽  
F. Canadas-Quesada ◽  
S. García-Galán ◽  
N. Ruiz-Reyes ◽  
P. Vera-Candeas ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Respiratory Sounds ◽  
Non Negative Matrix Factorization

scholarly journals Using a Panchromatic Image to Improve Hyperspectral Unmixing

2020 ◽  
Vol 12 (17) ◽  
pp. 2834
Author(s):  
Simon Rebeyrol ◽  
Yannick Deville ◽  
Véronique Achard ◽  
Xavier Briottet ◽  
Stephane May
Keyword(s):  
Matrix Factorization ◽  
State Of The Art ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Endmember Extraction ◽  
Local Approach ◽  
Hyperspectral Unmixing ◽  
Panchromatic Image ◽  
Art Methods ◽  
Non Negative Matrix Factorization

Hyperspectral unmixing is a widely studied field of research aiming at estimating the pure material signatures and their abundance fractions from hyperspectral images. Most spectral unmixing methods are based on prior knowledge and assumptions that induce limitations, such as the existence of at least one pure pixel for each material. This work presents a new approach aiming to overcome some of these limitations by introducing a co-registered panchromatic image in the unmixing process. Our method, called Heterogeneity-Based Endmember Extraction coupled with Local Constrained Non-negative Matrix Factorization (HBEE-LCNMF), has several steps: a first set of endmembers is estimated based on a heterogeneity criterion applied on the panchromatic image followed by a spectral clustering. Then, in order to complete this first endmember set, a local approach using a constrained non-negative matrix factorization strategy, is proposed. The performance of our method, in regards of several criteria, is compared to those of state-of-the-art methods obtained on synthetic and satellite data describing urban and periurban scenes, and considering the French HYPXIM/HYPEX2 mission characteristics. The synthetic images are built with real spectral reflectances and do not contain a pure pixel for each endmember. The satellite images are simulated from airborne acquisition with the spatial and spectral features of the mission. Our method demonstrates the benefit of a panchromatic image to reduce some well-known limitations in unmixing hyperspectral data. On synthetic data, our method reduces the spectral angle between the endmembers and the real material spectra by 46% compared to the Vertex Component Analysis (VCA) and N-finder (N-FINDR) methods. On real data, HBEE-LCNMF and other methods yield equivalent performance, but, the proposed method shows more robustness over the data sets compared to the tested state-of-the-art methods. Moreover, HBEE-LCNMF does not require one to know the number of endmembers.

scholarly journals A Quantum-inspired Classical Algorithm for Separable Non-negative Matrix Factorization

2019 ◽  
Author(s):  
Zhihuai Chen ◽  
Yinan Li ◽  
Xiaoming Sun ◽  
Pei Yuan ◽  
Jialin Zhang
Keyword(s):  
Image Processing ◽  
Polynomial Time ◽  
Matrix Factorization ◽  
Text Analysis ◽  
Low Rank ◽  
Nonnegative Matrices ◽  
Classical Algorithm ◽  
Data Points ◽  
Conical Hull ◽  
Non Negative Matrix Factorization

Non-negative Matrix Factorization (NMF) asks to decompose a (entry-wise) non-negative matrix into the product of two smaller-sized nonnegative matrices, which has been shown intractable in general. In order to overcome this issue, separability assumption is introduced which assumes all data points are in a conical hull. This assumption makes NMF tractable and widely used in text analysis and image processing, but still impractical for huge-scale datasets. In this paper, inspired by recent development on dequantizing techniques, we propose a new classical algorithm for separable NMF problem. Our new algorithm runs in polynomial time in the rank and logarithmic in the size of input matrices, which achieves an exponential speedup in the low-rank setting.

scholarly journals Predicting lincRNA-Disease Association in Heterogeneous Networks Using Co-regularized Non-negative Matrix Factorization

2021 ◽  
Vol 11 ◽  
Author(s):  
Yong Lin ◽  
Xiaoke Ma
Keyword(s):  
Matrix Factorization ◽  
Disease Gene ◽  
State Of The Art ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Ribonucleic Acids ◽  
Genetic Interaction Network ◽  
Gene Associations ◽  
Shed Light ◽  
Non Negative Matrix Factorization

Long intergenic non-coding ribonucleic acids (lincRNAs) are critical regulators for many complex diseases, and identification of disease-lincRNA association is both costly and time-consuming. Therefore, it is necessary to design computational approaches to predict the disease-lincRNA associations that shed light on the mechanisms of diseases. In this study, we develop a co-regularized non-negative matrix factorization (aka Cr-NMF) to identify potential disease-lincRNA associations by integrating the gene expression of lincRNAs, genetic interaction network for mRNA genes, gene-lincRNA associations, and disease-gene associations. The Cr-NMF algorithm factorizes the disease-lincRNA associations, while the other associations/interactions are integrated using regularization. Furthermore, the regularization does not only preserve the topological structure of the lincRNA co-expression network, but also maintains the links “lincRNA → gene → disease.” Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of accuracy on predicting the disease-lincRNA associations. The model and algorithm provide an effective way to explore disease-lncRNA associations.

Swarm Intelligence for Non-Negative Matrix Factorization

2011 ◽  
Vol 2 (4) ◽  
pp. 12-34 ◽  
Author(s):  
Andreas Janecek ◽  
Ying Tan
Keyword(s):  
Swarm Intelligence ◽  
Matrix Factorization ◽  
Classification Accuracy ◽  
Approximation Error ◽  
Synthetic Data ◽  
Low Rank ◽  
Low Rank Approximation ◽  
Approximation Quality ◽  
Initialization Strategy ◽  
Non Negative Matrix Factorization

The Non-negative Matrix Factorization (NMF) is a special low-rank approximation which allows for an additive parts-based and interpretable representation of the data. This article presents efforts to improve the convergence, approximation quality, and classification accuracy of NMF using five different meta-heuristics based on swarm intelligence. Several properties of the NMF objective function motivate the utilization of meta-heuristics: this function is non-convex, discontinuous, and may possess many local minima. The proposed optimization strategies are two-fold: On the one hand, a new initialization strategy for NMF is presented in order to initialize the NMF factors prior to the factorization; on the other hand, an iterative update strategy is proposed, which improves the accuracy per runtime for the multiplicative update NMF algorithm. The success of the proposed optimization strategies are shown by applying them on synthetic data and data sets coming from the areas of spam filtering/email classification, and evaluate them also in their application context. Experimental results show that both optimization strategies are able to improve NMF in terms of faster convergence, lower approximation error, and better classification accuracy. Especially the initialization strategy leads to significant reductions of the runtime per accuracy ratio for both, the NMF approximation as well as the classification results achieved with NMF.

scholarly journals State of the Art Review of Cell Therapy in the Treatment of Lung Disease, and the Potential for Aerosol Delivery

2020 ◽  
Vol 21 (17) ◽  
pp. 6435
Author(s):  
Hosanna Brave ◽  
Ronan MacLoughlin
Keyword(s):  
Cell Therapy ◽  
Therapeutic Potential ◽  
State Of The Art ◽  
Primary Objective ◽  
Pulmonary Diseases ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Cell Therapies ◽  
Routes Of Administration ◽  
Advanced Therapy

Respiratory and pulmonary diseases are among the leading causes of death globally. Despite tremendous advancements, there are no effective pharmacological therapies capable of curing diseases such as COPD (chronic obstructive pulmonary disease), ARDS (acute respiratory distress syndrome), and COVID-19. Novel and innovative therapies such as advanced therapy medicinal products (ATMPs) are still in early development. However, they have exhibited significant potential preclinically and clinically. There are several longitudinal studies published, primarily focusing on the use of cell therapies for respiratory diseases due to their anti-inflammatory and reparative properties, thereby hinting that they have the capability of reducing mortality and improving the quality of life for patients. The primary objective of this paper is to set out a state of the art review on the use of aerosolized MSCs and their potential to treat these incurable diseases. This review will examine selected respiratory and pulmonary diseases, present an overview of the therapeutic potential of cell therapy and finally provide insight into potential routes of administration, with a focus on aerosol-mediated ATMP delivery.

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