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12th International Conference on Advanced Computational Intelligence (ICACI)

作者： Li, Xinqi Wang, Jun Kwong, Sam City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Institude Shenzhen Peoples R China

ISBN: (数字)9781728142487

ISBN: (纸本)9781728142487

sparse nonnegative matrix factorization (SNMF) attracts much attention in the past two decades because its sparse and part-based representations are desirable in many machine learning applications. Due to the combinatorial nature of the sparsity constraint in form of l(0)-norm, the problem is hard to solve. In this paper, a hyperbolic tangent function is introduced to approximate the l(0)-norm. A discrete-time neurodynamic approach is developed for solving the proposed formulation. The stability and the convergence behavior are shown for the state vectors. Experiment results are discussed to demonstrate the superiority of the approach. The results show that this approach outperforms other sparse NMF approaches with the smallest relative reconstruction error and the required level of sparsity.

关键词： neurodynamic optimization sparse nonnegative matrix factorization

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sparse nonnegative matrix factorization Based on Collaborative Neurodynamic Optimization 9

Sparse Nonnegative Matrix Factorization Based on Collaborati...

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9th International Conference on Information Science and Technology (ICIST)

作者： Che, Hangjun Wang, Jun City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Hong Kong Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

ISBN: (纸本)9781728121062

This paper presents a collaborative neurodynamic approach to sparse nonnegative matrix factorization (SNMF). SNMF is formulated as a bilevel optimization problem. In the lower level of the problem, the sparsity of factorized matrix is minimized subject to the factorization error and nonnegative constraints. In the upper level of the problem, the parameter of the inverted Gaussian function is minimized to approximate l(0) norm. A group of neurodynamic models operating at two timescales is employed to solve the reformulated problem. The experimental results show the superiority of the proposed approach.

关键词： sparse nonnegative matrix factorization Bilevel optimization Collaborative neurodynamic approach

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Band selection using sparse nonnegative matrix factorization with the thresholded Earth's mover distance for hyperspectral imagery classification

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EARTH SCIENCE INFORMATICS 2015年第4期8卷 907-918页

作者： Sun, Weiwei Li, Weiyue Li, Jialin Lai, Yenming Mark Ningbo Univ Coll Architectural Engn Civil Engn & Environm Ningbo 315211 Zhejiang Peoples R China Shanghai Normal Univ Inst Urban Dev Shanghai 200234 Peoples R China Univ Texas Austin Inst Computat Engn & Sci Austin TX 78712 USA

A sparse nonnegative matrix factorization method with the thresholded ground distance (SNMF-TEMD) is proposed to solve the band selection problem in hyperspectral imagery (HSI) classification. The SNMF-TEMD assumes that band vectors are sampled from a union of low-dimensional subspaces and approximates a HSI data matrix with the product of a basis matrix constructed from subspaces and a sparse coefficient matrix. The SNMF-TEMD utilizes the TEMD metric to better measures approximation errors during the optimization of HSI data factorization. The TEMD metric makes up the theoretical drawbacks in the Euclidean distance (ED) and Kullback-Leibler divergence (KLD) metrics when measuring the approximation errors in HSI datasets. The SNMF-TEMD is solved by the combination of min-cost-flow algorithm and multiplicative update rules. The band cluster assignments are found according to positions of largest entries in columns of the coefficient matrix and the desired band subset constitutes with the bands closest to their cluster centers. Three groups of experiments on two HSI datasets are performed to explore the performance of SNMF-TEMD. Four popular band selection methods are used to make comparisons: affinity propagation (AP), maximum-variance principal component analysis (MVPCA), SNMF with ED metric (SNMF-ED) and SNMF with KLD metric (SNMF-KLD). Experimental results show that SNMF-TEMD outperforms all four methods in classification accuracy and its computational speed is slower than SNMF-ED and SNMF-KLD. SNMF-TEMD is a better choice for band selection among all five methods because of its overwhelming advantage in classification and the popular speed remedy scheme from parallel computing and high-performance computers.

关键词： Band selection sparse nonnegative matrix factorization Thresholded earth's mover distance Hyperspectral imagery Classification

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Data Classification with Ensembles of One-Class Support Vector Machines and sparse nonnegative matrix factorization 7

Data Classification with Ensembles of One-Class Support Vect...

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7th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Cyganek, Boguslaw Krawczyk, Bartosz AGH Univ Sci & Technol Al Mickiewicza 30 PL-30059 Krakow Poland Wroclaw Univ Technol PL-50370 Wroclaw Poland

ISBN: (纸本)9783319157023;9783319157016

The paper presents a method for data classification with ensemble of one-class classifiers based on data segmentation. Each data class is partitioned with the nonnegative matrix factorization (NMF) algorithm with sparse constraints. It allows splitting of the input data into compact and consistent data clusters with automatic determination of a number of clusters. Data partitions are fed to an ensemble composed of a number of one-class support vector machine (SVM) classifiers. The proposed method shows high accuracy and fast classification.

关键词： Ensemble of classifiers One-class support vector machine sparse nonnegative matrix factorization

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ADAPTIVE ENDMEMBER EXTRACTION BASED sparse nonnegative matrix factorization WITH SPATIAL LOCAL INFORMATION

ADAPTIVE ENDMEMBER EXTRACTION BASED SPARSE NONNEGATIVE MATRI...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Li, Huali Li, Shutao Zhang, Liangpei Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Peoples R China

ISBN: (纸本)9781479979295

Hyperspectral Unmixing aims at getting the endmember signature and their corresponding abundance maps from highly mixed Hyperspectral image. nonnegative matrix factorization (NMF) is a widely used method for spectral unmixing because it can obtain better performance while there is no pure pixels in the hyperspectral image. However, many methods based on nonnegative matrix factorization seldom consider the spatial information both on local and nonlocal. To combine the spatial and spectral information together to improve the unmixing accuracy, an adaptive endmember extraction based sparse nonnegative matrix factorization with spatial local information (ASNMF) is proposed in this paper. A superpixel segmentation is to obtain many meaningful regions which are spectral similar and spatial adjacent. Endmember is adaptively extracted on each superpixel to generate endmember set. Initialing the endmember set, ASNMF could adaptively obtain the final endmembers with the sparse nonnegative matrix factorization. Both the experiments on synthetic and real scene images show the effectiveness of the proposed method for hyperspectral unmixing.

关键词： adaptive endmember extraction sparse nonnegative matrix factorization hyperspectral remote sensing imagery

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ADAPTIVE ENDMEMBER EXTRACTION BASED sparse nonnegative matrix factorization WITH SPATIAL LOCAL INFORMATION

ADAPTIVE ENDMEMBER EXTRACTION BASED SPARSE NONNEGATIVE MATRI...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Huali Li Shutao Li Liangpei Zhang Coll. of Electr. & Inf. Eng. Hunan Univ. Changsha China

ISBN: (纸本)9781479979301

关键词： geophysical image processing geophysical techniques hyperspectral imaging matrix decomposition ASNMF adaptive endmember extraction endmember set endmember signature hyperspectral image unmixing matrix factorization sparse nonnegative matrix factorization spatial information spatial local information spectral information superpixel segmentation Data mining Hyperspectral imaging Image segmentation Mathematical model sparse matrices hyperspectral remote sensing imagery matrix decomposition hyperspectral imagery sparse matrix geophysical techniques image segmentation nonnegative data mining Mathematical Model exsolution hyperspectral remote sensing local information.

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Ultrasound image analysis technology under deep belief networks in evaluation on the effects of diagnosis and chemotherapy of cervical cancer

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JOURNAL OF SUPERCOMPUTING 2021年第4期77卷 4151-4171页

作者： Zhou, Hongzhen Wang, Shuyuan Zhang, Tao Liu, Demei Yang, Kevin Linyi Canc Hosp Dept Ultrasound Linyi 276000 Shandong Peoples R China Linyi Luozhuang Cent Hosp Dept Ultrasound Linyi 276017 Shandong Peoples R China Linyi Canc Hosp Dept Gynecol Oncol 1 Linyi 276000 Shandong Peoples R China Imperial Coll London London SW7 2AZ England

The purpose of this study was to explore the value of extraction of tumor features in contrast-enhanced ultrasonography (CEUS) images based on the deep belief networks (DBN) for the diagnosis of cervical cancer patients and realize the intelligent evaluation on effects of diagnosis and chemotherapy of the cervical cancer. An automatic extraction algorithm with the time-intensity curve (TIC) was proposed based on sparse nonnegative matrix factorization (SNMF) in this study, and was applied to the framework of automatic analysis of cervical cancer tumors based on the deep belief networks, to assist doctors in the analysis of cervical cancer tumors. The framework was applied to the real clinical diagnostic data, and the feasibility of the method was verified by comparing the accuracy, sensitivity, and specificity. Later, the parameters of patients' time to peak (TP), peak intensity (PI), mean transit time (MTT), and area under the curve (AUC) were obtained by drawing TICs, and the changes of p53 protein and ki-67 protein obtained by pathological section staining were analyzed to evaluate the therapeutic effect in the patients. It was found that the proposed model of tumor feature extraction based on the DBN had the higher accuracy (86.36%), sensitivity (83.33%), and specificity (87.50%). The related parameters of TIC curve obtained based on SNMF showed that there was a significant difference in p53 content between tissues with different degrees of disease (p < 0.05), the PI of poorly differentiated tissues was significantly higher than that of those with high to medium differentiation (p < 0.05). In addition, PI and AUC of patients after chemotherapy were significantly lower than that before chemotherapy (p < 0.05), while MTT was significantly higher than that before chemotherapy (p < 0.05). Therefore, the proposed TIC feature extraction of CEUS images based on SNMF and the automatic tumor classification based on deep learning can be used in the diagnosis and efficacy

关键词： sparse nonnegative matrix factorization Deep belief networks Time-intensity curve Feature extraction Cervical cancer

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sparse NMF with archetypal regularization: computational and robustness properties

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 1726-1787页

作者： Kayhan Behdin Rahul Mazumder Operations Research Center Massachusetts Institute of Technology Cambridge MA MIT Sloan School of Management and Operations Research Center Massachusetts Institute of Technology Cambridge MA

We consider the problem of sparse nonnegative matrix factorization (NMF) using archetypal regularization. The goal is to represent a collection of data points as nonnegative linear combinations of a few nonnegative sparse factors with appealing geometric properties, arising from the use of archetypal regularization. We generalize the notion of robustness studied in Javadi and Montanari (2019) (without sparsity) to the notions of (a) strong robustness that implies each estimated archetype is close to the underlying archetypes and (b) weak robustness that implies there exists at least one recovered archetype that is close to the underlying archetypes. Our theoretical results on robustness guarantees hold under minimal assumptions on the underlying data, and applies to settings where the underlying archetypes need not be sparse. We present theoretical results and illustrative examples to strengthen the insights underlying the notions of robustness. We propose new algorithms for our optimization problem; and present numerical experiments on synthetic and real data sets that shed further insights into our proposed framework and theoretical developments.

关键词： sparse nonnegative matrix factorization archetypal analysis robustness to perturbation model misspecification nonconvex optimization

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Evaluation of sparsity metrics and evolutionary algorithms applied for normalization of H&E histological images

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PATTERN ANALYSIS AND APPLICATIONS 2024年第1期27卷 11-11页

作者： Tosta, Thaina A. Azevedo de Faria, Paulo Rogerio Neves, Leandro Alves Martins, Alessandro Santana Kaushal, Chetna do Nascimento, Marcelo Zanchetta Univ Fed Sao Paulo Inst Sci & Technol Ave Cesare Mansueto Giulio Lattes 1201 BR-12247014 Sao Jose Dos Campos SP Brazil Univ Fed Uberlandia Inst Biomed Sci Dept Histol & Morphol Ave Amazonas S-N BR-38405320 Uberlandia MG Brazil Sao Paulo State Univ Dept Comp Sci & Stat R Cristovao Colombo 2265 BR-15054000 Sao Jose Do Rio Preto SP Brazil Fed Inst Triangulo Mineiro R Belarmino Vilela Junqueira S-N BR-38305200 Ituiutaba MG Brazil Chitkara Univ Chitkara Univ Inst Engn & Technol Rajpura 140401 Punjab India Univ Fed Uberlandia Fac Comp Ave Joao Naves de Avila 2121 BR-38400902 Uberlandia MG Brazil

Color variations in H&E histological images can impact the segmentation and classification stages of computational systems used for cancer diagnosis. To address these variations, normalization techniques can be applied to adjust the colors of histological images. Estimates of stain color appearance matrices and stain density maps can be employed to carry out these color adjustments. This study explores these estimates by leveraging a significant biological characteristic of stain mixtures, which is represented by a sparsity parameter. Computationally estimating this parameter can be accomplished through various sparsity measures and evolutionary algorithms. Therefore, this study aimed to evaluate the effectiveness of different sparsity measures and algorithms for color normalization of H&E-stained histological images. The results obtained demonstrated that the choice of different sparsity measures significantly impacts the outcomes of normalization. The sparsity metric l(epsilon)(0) proved to be the most suitable for it. Conversely, the evolutionary algorithms showed little variations in the conducted quantitative analyses. Regarding the selection of the best evolutionary algorithm, the results indicated that particle swarm optimization with a population size of 250 individuals is the most appropriate choice.

关键词： Histopathology image analysis H&E color normalization Sparsity estimation Evolutionary algorithms sparse nonnegative matrix factorization

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Multi-target ensemble learning based speech enhancement with temporal-spectral structured target

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APPLIED ACOUSTICS 2023年第1期205卷

作者： Wang, Wenbo Guo, Weiwei Liu, Houguang Yang, Jianhua Liu, Songyong China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Peoples R China Chinese Peoples Liberat Army Gen Hosp Coll Otolaryngol Head & Neck Surg Beijing 100853 Peoples R China Natl Clin Res Ctr Otolaryngol Dis Beijing 100853 Peoples R China Minist Educ Key Lab Hearing Sci Beijing 100853 Peoples R China

Recently, deep neural network (DNN)-based speech enhancement has shown considerable success, and mapping-based and masking-based are the two most commonly used methods. However, these methods do not consider the spectrum structures of signal. In this paper, a novel structured multi-target ensemble learning (SMTEL) framework is proposed, which uses target temporal-spectral structures to improve speech quality and intelligibility. First, the basis matrices of clean speech, noise, and ideal ratio mask (IRM) are captured by the sparse nonnegative matrix factorization, which contain the basic structures of the signal. Second, the basis matrices are co-trained with a multi-target DNN to estimate the activation matrices instead of directly estimating the targets. Then a joint training single layer perceptron is pro-posed to integrate the two targets and further improve speech quality and intelligibility. The sequential floating forward selection method is used to systematically analyze the impact of the integrated targets on enhanced performance, and analyze the effect of the target weights on the results. Finally, the pro-posed method with progressive learning is combined to improve the enhanced performance. Systematic experiments on the UW/NU corpus show that the proposed method achieves the best enhancement effect in the case of low network cost and complexity, especially in visible nonstationary noise environment. Compared with the target integration method which does not use structured targets and the long short-term memory masking method, the speech quality of the proposed method is improved by 25.6 % and 29.2 % of restaurant noise, and the speech intelligibility is improved by 35.5 % and 15.8 %, respectively.(c) 2023 Elsevier Ltd. All rights reserved.

关键词： Speech enhancement Temporal -spectral structured target Multi -target ensemble learning sparse nonnegative matrix factorization

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