Data Fusion by Matrix Factorization

Žitnik, Marinka; Zupan, Blaž

doi:10.1109/tpami.2014.2343973

Cited by 195 publications

(213 citation statements)

References 57 publications

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“…Moreover, a data fusion approach with penalized matrix tri-factorization (DFMF) is proposed to simultaneously decompose data matrices reveal hidden associations. This approach identifies that matrix factorization based data fusion achieves the high accuracy result and time response in a particular scenario [19]. To tackle the issues with sparse data, a context-aware tensor factorization (CATF) model has employed high-order singular value decomposition (HOSVD) to integrate with contextual features (e.g.…”

Section: A Matrix Factorizationmentioning

confidence: 99%

Data Fusion for MaaS: Opportunities and Challenges

Zhou

Cai³

et al. 2018

2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design ((CSCWD))

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Abstract-Computer Supported Cooperative Work (CSCW)in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling.

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Section: A Matrix Factorizationmentioning

confidence: 99%

Data Fusion for MaaS: Opportunities and Challenges

Zhou

Cai³

et al. 2018

2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design ((CSCWD))

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“…sets of genes, drugs, diseases, etc.) [135], where indices, i = j, 1 i, j N, denote different datasets. The relation matrices are simultaneously decomposed into low-dimensional factors, G i , G j and S ij , within the same optimization function.…”

Section: Non-negative Matrix Factorizationmentioning

confidence: 99%

“…These terms are also known as graph regularization terms [194,195]. For more details about the construction of the objective function and derivation of the multiplicative update rules, we refer the reader to references [135,192,193].…”

Section: Non-negative Matrix Factorizationmentioning

confidence: 99%

Methods for biological data integration: perspectives and challenges

Gligorijević¹,

Pržulj²

2015

J. R. Soc. Interface.

209

157

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Rapid technological advances have led to the production of different types of biological data and enabled construction of complex networks with various types of interactions between diverse biological entities. Standard network data analysis methods were shown to be limited in dealing with such heterogeneous networked data and consequently, new methods for integrative data analyses have been proposed. The integrative methods can collectively mine multiple types of biological data and produce more holistic, systems-level biological insights. We survey recent methods for collective mining (integration) of various types of networked biological data. We compare different stateof-the-art methods for data integration and highlight their advantages and disadvantages in addressing important biological problems. We identify the important computational challenges of these methods and provide a general guideline for which methods are suited for specific biological problems, or specific data types. Moreover, we propose that recent non-negative matrix factorization-based approaches may become the integration methodology of choice, as they are well suited and accurate in dealing with heterogeneous data and have many opportunities for further development.

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“…The authors combined gene expression and histological data from animals and human with protein-protein interactions and GO annotation to predict liver injury induced by chemicals. This was done based on a constrained matrix tri-factorization algorithm suggested by the same authors [98].…”

Section: Utilizing Integrated Omics Data For Personalized Medicine CLmentioning

confidence: 99%

Integrating Heterogeneous omics Data via Statistical Inference and Learning Techniques

Ahmad

Fröhlich

2016

Genomics Comput Biol

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Multi-omics studies are believed to provide a more comprehensive picture of a complex biological system than traditional studies with one omics data source. However, from a statistical point of view data integration implies non-trivial challenges. In this review, we highlight recent statistical inference and learning techniques that have been devised in this context. In the first part of our article, we focus on techniques to identify a relevant biological sub-system based on combined omics data. In the second part of our article we ask, in which way integrated omics data could be used for better personalized patient treatment in a supervised as well as unsupervised learning setting. Different classes of algorithms are discussed for both application tasks. Existing and future challenges for data integration methods are pointed out.

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Data Fusion by Matrix Factorization

Cited by 195 publications

References 57 publications

Data Fusion for MaaS: Opportunities and Challenges

Data Fusion for MaaS: Opportunities and Challenges

Methods for biological data integration: perspectives and challenges

Integrating Heterogeneous omics Data via Statistical Inference and Learning Techniques

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