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Locally linear embedding (LLE) is an effective nonlinear dimensionality reduction method for exploring the intrinsic characteristics of high dimensional data. This paper mainly proposes a hierarchical framework manifold learning method, based on LLE and growing neural gas (GNG), named growing locally linear embedding (GLLE). The proposed algorithm is able to preserve the global topological structures...
Manifold learning can discover the structure of high dimensional data and provides understanding of multidimensional patterns by preserving the local geometric characteristics. However, due to locality geometry preservation, manifold learning is sensitive to noise. To solve the noisy manifold learning problem, this paper proposes neighbor smoothing embedding (NSE) for noisy points sampled from a nonlinear...
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