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Hybridizing sparse component analysis with genetic algorithms for microarray analysis

NEUROCOMPUTING 2008年第10-12期71卷 2356-2376页

作者： Stadlthanner, K. Theis, F. J. Lang, E. W. Tome, A. M. Puntonet, C. G. Gorriz, J. M. Univ Regensburg Computat Intelligence Grp Inst Biophys Eupener Str 114 D-52066 Aachen Germany Univ Aveiro IEETA Dept Elect & TelecoMUN P-3810 Aveiro Portugal Univ Granada Dept Arqitectura & Tecnol Computadores Granada 18371 Spain

nonnegative matrix factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. However, it is known not to lead to unique results when applied to blind source separation (BSS) problems. In this paper we present an extension of NMF capable of solving the BSS problem when the underlying sources are sufficiently sparse. In contrast to most well-established BSS methods, the devised algorithm is capable of solving the BSS problem in cases where the underlying sources are not independent or uncorrelated. As the proposed fitness function is discontinuous and possesses many local minima, we use a genetic algorithm for its minimization. Finally, we apply the devised algorithm to real world microarray data. (C) 2008 Elsevier B.V. All rights reserved.

关键词： sparse nonnegative matrix factorization blind source separation gene microarray analysis

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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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SPECTRAL IMAGE PROCESSING USING sparse LINEAR TRANSFORMS

SPECTRAL IMAGE PROCESSING USING SPARSE LINEAR TRANSFORMS

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IEEE International Geoscience and Remote Sensing Symposium

作者： Robila, Stefan A. Montclair State Univ Dept Comp Sci Montclair NJ USA

ISBN: (纸本)9781424433940

We propose the employment of nonnegative sparse linear feature extraction as a tool for unsupervised spectral unmixing sparse feature extraction can be seen as a general linear unmixing approach that maps the data into a new dimensional space in which each of the components has only a limited number of non-zero values Unlike other transforms that target decorrelation or statistical independence, our focus is on the enforcement of sparseness by imposing restrictions (such as cardinality or norm relationships), as well as nonnegativity. When compared to the linear mixing model, the sparse components can be naturally associated to the abundance of endmembers, and the inverse transform to the endmembers. Our approach is a variant of a well known technique based on nonnegative matrix factorization (NMF). In most of the cases. the NMF components are produced using a gradient descent optimization algorithm that was previously shown to converge. To validate our approach we use quantitative (classification) and qualitative (visualization) analysis of hyperspectral data sets.

关键词： Hyperspectral imagery Linear Mixing Model sparse nonnegative matrix factorization

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Exploring matrix factorization techniques for classification of gene expression profiles

Exploring matrix factorization techniques for classification...

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IEEE International Symposium on Intelligent Signal Processing

作者： Schachtner, R. Lutter, D. Tome, A. M. Lang, E. W. Gomez Vilda, P. Univ Regensburg Inst Biophys D-93040 Regensburg Germany Univ Aveiro DETUA IEETA P-3810193 Aveiro Portugal Univ Politecn Madrid UPM DATSI E-18500 Madrid Spain

ISBN: (纸本)9781424408290

In this study we focus on diagnostic classification tasks and the extraction of related marker genes from gene expression profiles. We apply ICA and sparse NMF to various microarray data sets. The latter monitor the gene expression levels of either human breast cancer (HBC) cell lines [1] or the famous leucemia data set [2] under various environmental conditions. We show that these matrix decomposition techniques are able to identify relevant signatures in the deduced matrices and extract marker genes from these gene expression profiles. With these marker genes corresponding test data sets can be classified into related diagnostic categories.

关键词： independent component analysis sparse nonnegative matrix factorization gene expression profiles

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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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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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Research of Cluster Feature Extraction and Evaluation System Construction for Mixed Teaching Data 2020

Research of Cluster Feature Extraction and Evaluation System...

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Proceedings of the 2020 4th International Symposium on Computer Science and Intelligent Control

作者： Jing Zhou Jun Xiong Ze Chen School of Artificial Intelligence Jianghan University Wuhan China

ISBN: (纸本)9781450388894

At present, the mining and analysis of teaching data is mainly aimed at the online courses data, but not mixed data, which is fused by the traditional offline-classroom and online teaching data. Meanwhile, the most evaluation models are constructed by the learning data to evaluate the teaching quality of teachers, but not to evaluate and grade the individual quality of students. In fact, the evaluation and grading of students' quality can effectively provide more targeted teaching intervention for students of different levels based on the data analysis. To address these issues, the online teaching data is fused by the students' learning behavior data of traditional course to form the mixed data in this paper, and then the sparse non-negative matrix factorization (SNMF) method is adopted to extract the feature clusters of mixed learning data. According to the weights of the extracted cluster features, the multi-level feature indicators are selected in turn to construct the hierarchical evaluation index system. Finally, the comprehensive weighting method is adopted to evaluate and grade the individual students. In this paper, the mixed teaching data of computer basic course of our school is formed, and then the weights of feature clusters are calculated by SNMF and an evaluation model is established to evaluate and grade the students. The grading results are in accordance with the normal distribution and basically consistent with the grading distribution of students' final examination scores. Thus the validity of the model and method proposed in this paper is proved.

关键词： Evaluation Index System Feature Cluster Feature Extraction Mixed Data sparse nonnegative matrix factorization

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