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• W2517823715 abstract "Various Dimensionality Reduction algorithms transform initial high-dimensional data into their lower-dimensional representations preserving chosen properties of the initial data. Typically, such algorithms use the solution of large-dimensional optimization problems, and the incremental versions are designed for many popular algorithms to reduce their computational complexity. Under manifold assumption about high-dimensional data, advanced manifold learning algorithms should preserve the Data manifold and its differential properties such as tangent spaces, Riemannian tensor, etc. Incremental version of the Grassmann&Stiefel Eigenmaps manifold learning algorithm, which has asymptotically minimal reconstruction error, is proposed in this paper and has significantly smaller computational complexity in contrast to the initial algorithm." @default.
• W2517823715 created "2016-09-16" @default.
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• W2517823715 date "2016-01-01" @default.
• W2517823715 modified "2023-09-27" @default.
• W2517823715 title "Incremental Construction of Low-Dimensional Data Representations" @default.
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• W2517823715 doi "https://doi.org/10.1007/978-3-319-46182-3_5" @default.
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