@article{3024739,
    title = "Discovering the whole by the coarse: A topological paradigm for data analysis",
    author = "Krim, H. and Gentimis, T. and Chintakunta, H.",
    journal = "IEEE Signal Processing Magazine",
    year = "2016",
    volume = "33",
    number = "2",
    pages = "95-104",
    publisher = "Institute of Electrical and Electronics Engineers, Inc. (IEEE)",
    issn = "1053-5888, 1558-0792",
    doi = "10.1109/MSP.2015.2510703",
    keywords = "Graphic methods, Big data applications;  Graph-based models, Big data",
    abstract = "The increasing interest in big data applications is ushering in a large effort in seeking new, efficient, and adapted data models to reduce complexity, while preserving maximal intrinsic information. Graph-based models have recently been getting a lot of attention on account of their intuitive and direct connection to the data [43]. The cost of these models, however, is to some extent giving up geometric insight as well as algebraic flexibility. © 2016 IEEE."
}