Abstract
Data embedding (DE) and graph visualization (GV) methods are very compatible tools used in Exploratory Data Analysis for visualization of complex data such as high-dimensional data and complex networks. However, high computational complexity and memory load of existing DE and GV algorithms, considerably hinders visualization of truly large and big data consisting of as many as M~106+ data objects and N~103+ dimensions. Recently, we have shown that by employing only a small fraction of distances between data objects one can obtain very satisfactory reconstruction of topology of a complex data in 2D in a linear-time O(M). In this paper, we demonstrate the high robustness of our approach. We show that even poor approximations of the nn-nearst neighbor graph, representing high-dimensional data, can yield acceptable data embeddings. Furthermore, some incorrectness in the nearest neighbor list can often be useful to improve the quality of data visualization. This robustness of our DE method, together with its high memory and time efficiency, meets perfectly the requirements of big and distributed data visualization, when finding the accurate nearest neighbor list represents a great computational challenge.
13th International Conference on Machine Learning and Data Mining MLDM, New York, July 15-20, 2017.
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Acknowledgments
This research is supported by the Polish National Center of Science (NCN) DEC-2013/09/B/ST6/01549.
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Dzwine, W., Wcisło, R. (2017). ivhd: A Robust Linear-Time and Memory Efficient Method for Visual Exploratory Data Analysis. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2017. Lecture Notes in Computer Science(), vol 10358. Springer, Cham. https://doi.org/10.1007/978-3-319-62416-7_25
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DOI: https://doi.org/10.1007/978-3-319-62416-7_25
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