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- W3016029206 abstract "Manifold learning is a recent popular approach to find the internal low-dimensional structure from the high-dimensional nonlinear data sets embedded in the European space. It is based on the idea that the data we can observe are actually mapped from a low-dimensional space to a high-dimensional one. Given the limitation of the internal characteristics of the data, those artificial high-dimensional data produce dimensional redundancy. In fact, these data can be represented in the lower-dimension space. Algorithms for this task are to achieve efficient and accurate data analysis results. After studying some common algorithms purposed for manifold learning, we find that there is still an improvement for their efficiency. To be specific, in order to reduce computational complexity of the Isometrical Mapping, we introduce a new solution through the concept of data nuggets” to do data partition and selection. This concept is to reduce a large dataset into a smaller set of nuggets of data, each of which contains a center, weight and a scale parameter. While the data is re-expressed as data nuggets, we can apply algorithms for Isomap. This combination can largely reduce the computational complexity due to the decreased dataset. Meanwhile, it still maintains the advantages of Isomap that keep the geodesic distances between the points unchanged." @default.
- W3016029206 created "2020-04-17" @default.
- W3016029206 creator A5062451280 @default.
- W3016029206 date "2019-12-01" @default.
- W3016029206 modified "2023-09-23" @default.
- W3016029206 title "Advanced Isomap Based on Data Nuggets Algorithm" @default.
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- W3016029206 doi "https://doi.org/10.1109/iccc47050.2019.9064472" @default.
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