|Mittagsseminar Talk Information|
Date and Time: Tuesday, January 15, 2013, 12:15 pm
Duration: 30 minutes
Location: CAB G51
Speaker: Hemant Tyagi
Numerous dimensionality reduction problems in data analysis involve the recovery of low-dimensional models or the learning of manifolds underlying sets of data. Many manifold learning methods require the estimation of the tangent space of the manifold at a point from locally available data samples. Local sampling conditions such as (i) the size of the neighborhood and (ii) the number of samples in the neighborhood affect the performance of learning algorithms. In this work, we propose a theoretical analysis of local sampling conditions for the estimation of the tangent space at a point P lying on a m-dimensional Riemannian manifold S in R^n. Assuming a smooth embedding of S in R^n, we estimate the tangent space by performing a Principal Component Analysis (PCA) on points sampled from the neighborhood of P on S.
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