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- W2220340178 abstract "T-SNE is a well-known approach to embedding high-dimensional data. The basic assumption of t-SNE is that the data are non-constrained in the Euclidean space and the local proximity can be modelled by Gaussian distributions. This assumption does not hold for a wide range of data types in practical applications, for instance spherical data for which the local proximity is better modelled by the von Mises-Fisher (vMF) distribution instead of the Gaussian. This paper presents a vMF-SNE embedding algorithm to embed spherical data. An iterative process is derived to produce an efficient embedding. The results on a simulation data set demonstrated that vMF-SNE produces better embeddings than t-SNE for spherical data." @default.
- W2220340178 created "2016-06-24" @default.
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- W2220340178 date "2016-03-01" @default.
- W2220340178 modified "2023-09-24" @default.
- W2220340178 title "VMF-SNE: Embedding for spherical data" @default.
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- W2220340178 doi "https://doi.org/10.1109/icassp.2016.7472096" @default.
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