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In this paper we present a powerful approach for noisy data reconstruction and also for data compression based on our algorithms for tensor factorization and decomposition [10], [9]. This approach has many potential applications in computational neuroscience, multi-sensory, multidimensional data analysis and text mining. Our algorithms are locally stable and work well for sufficiently sparse data...
The decomposition and reconstruction approach has been proposed for 3D subdivision surface by using two-dimensional discrete wavelet transform. Both high frequency filter and low frequency filter have been applied in order to obtain the simulation of the original surface. The error generated during this process proves to be just high frequency filter. In this way the error range of detail surface...
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