Multi-view clustering via multi-manifold regularized non-negative matrix factorization

Zong, Linlin; Zhang, Xianchao; Zhao, Long; Yu, Hong; Zhao, Qianli

doi:10.1016/j.neunet.2017.02.003

Cited by 225 publications

(49 citation statements)

References 28 publications

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“…Now we cluster across the two views simultaneously to find a common latent structure. Among the multi-view clustering algorithms, NMF based methods [ 14 , 19 , 20 ] have demonstrated strong vitality and efficiency. Based on the two feature matrices we use a multi-view NMF model [ 14 ] to find a common coefficient (or basis) matrix.…”

Section: Methodsmentioning

confidence: 99%

Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks

Wang

Gao

et al. 2018

BMC Bioinformatics

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BackgroundComprehensive analyzing multi-omics biological data in different conditions is important for understanding biological mechanism in system level. Multiple or multi-layer network model gives us a new insight into simultaneously analyzing these data, for instance, to identify conserved functional modules in multiple biological networks. However, because of the larger scale and more complicated structure of multiple networks than single network, how to accurate and efficient detect conserved functional biological modules remains a significant challenge.ResultsHere, we propose an efficient method, named ConMod, to discover conserved functional modules in multiple biological networks. We introduce two features to characterize multiple networks, thus all networks are compressed into two feature matrices. The module detection is only performed in the feature matrices by using multi-view non-negative matrix factorization (NMF), which is independent of the number of input networks. Experimental results on both synthetic and real biological networks demonstrate that our method is promising in identifying conserved modules in multiple networks since it improves the accuracy and efficiency comparing with state-of-the-art methods. Furthermore, applying ConMod to co-expression networks of different cancers, we find cancer shared gene modules, the majority of which have significantly functional implications, such as ribosome biogenesis and immune response. In addition, analyzing on brain tissue-specific protein interaction networks, we detect conserved modules related to nervous system development, mRNA processing, etc.ConclusionsConMod facilitates finding conserved modules in any number of networks with a low time and space complexity, thereby serve as a valuable tool for inference shared traits and biological functions of multiple biological system.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2434-5) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks

Wang

Gao

et al. 2018

BMC Bioinformatics

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“…Locally linear embedding (LLE) is a well-known algorithm for non-linear dimensionality reduction. Based on the LLE [ 31 ], each sample point in the space can be expressed by a weight matrix in the high-dimensional space, which provides the description of the data points in the low-dimensional space [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform

Wei

Xiao

Wang

et al. 2017

Sensors

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In this study, a portable electronic nose (E-nose) was self-developed to identify rice wines with different marked ages—all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and the obtained response values were transmitted to the Smartphone thorough a wireless communication module. Then, Aliyun worked as a cloud storage platform for the storage of responses and identification models. The measurement of the E-nose was composed of the taste information obtained phase (TIOP) and the aftertaste information obtained phase (AIOP). The area feature data obtained from the TIOP and the feature data obtained from the TIOP-AIOP were applied to identify rice wines by using pattern recognition methods. Principal component analysis (PCA), locally linear embedding (LLE) and linear discriminant analysis (LDA) were applied for the classification of those wine samples. LDA based on the area feature data obtained from the TIOP-AIOP proved a powerful tool and showed the best classification results. Partial least-squares regression (PLSR) and support vector machine (SVM) were applied for the predictions of marked ages and SVM (R2 = 0.9942) worked much better than PLSR.

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“…To obtain a better understanding of these complex phenomena, by integrating different views of the data, many multiview clustering algorithms have been proposed (see Section 6.4 for details on multiview learning techniques). Some examples are the methods based on matrix factorization that integrate clustering solutions obtained on each single view (Zong, Zhang, Zhao, Yu, & Zhao, 2017). Other approaches use modifications of the classical K-means clustering algorithm (Chen, Xiaofei, Huang, & Ye, 2013;Xu, Han, Nie, & Li, 2017).…”

Section: Multiview Clusteringmentioning

confidence: 99%

Machine learning for bioinformatics and neuroimaging

Serra

Galdi

Tagliaferri

2018

WIREs Data Min & Knowl

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Machine Learning (ML) is a well‐known paradigm that refers to the ability of systems to learn a specific task from the data and aims to develop computer algorithms that improve with experience. It involves computational methodologies to address complex real‐world problems and promises to enable computers to assist humans in the analysis of large, complex data sets. ML approaches have been widely applied to biomedical fields and a great body of research is devoted to this topic. The purpose of this article is to present the state‐of‐the art in ML applications to bioinformatics and neuroimaging and motivate research in new trend‐setting directions. We show how ML techniques such as clustering, classification, embedding techniques and network‐based approaches can be successfully employed to tackle various problems such as gene expression clustering, patient classification, brain networks analysis, and identification of biomarkers. We also present a short description of deep learning and multiview learning methodologies applied in these contexts. We discuss some representative methods to provide inspiring examples to illustrate how ML can be used to address these problems and how biomedical data can be characterized through ML. Challenges to be addressed and directions for future research are presented and an extensive bibliography is included. This article is categorized under: Application Areas > Health Care Technologies > Computational Intelligence Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining

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Multi-view clustering via multi-manifold regularized non-negative matrix factorization

Cited by 225 publications

References 28 publications

Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks

Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks

Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform

Machine learning for bioinformatics and neuroimaging

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