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Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in addressing collaborative filtering (CF) problems. During the MF process, the non-negativity, which ensures good representativeness of the learnt model, is critically important. However, current non-negative MF (NMF) models are mostly designed for problems in computer vision, while CF problems differ from them due...
In the procedure of hyperspectral data dimensionality reduction (DR), intrinsic dimensionality (ID) of high-dimensional hyperspectral data is normally obtained through the linear dimensionality analysis methods. This article applies a kind of unsupervised learning method, manifold learning method, to the dimensionality analysis for hyperspectral data and gives a manifold-learning-based algorithm for...
While reducing the dimensionality of hyperspectral data, linear dimensionality analysis methods are usually adopted to acquire intrinsic dimensionality (ID) of high-dimensional hyperspectral data. This paper uses an unsupervised manifold learning method to conduct the dimensionality analysis of hyperspectral data, providing a manifold-learning-based algorithm for hyperspectral data dimensionality...
Overlapped fiber separation is one of main procedures in cross-sectional fiber analysis. In the proposed distance transform based peeling algorithm, a separation point is determined by removing points with the least distance. The overlapped fibers in cross-section are separated by the separation point. Experiments show that the proposed algorithm precisely separates overlapped fibers of various shapes.
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