A Modified Semi-Supervised Learning Algorithm on Laplacian Eigenmaps
Neural Processing Letters
Dimension reduction and visualization of large high-dimensional data via interpolation
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Incremental manifold learning by spectral embedding methods
Pattern Recognition Letters
Supervised subspace projections for constructing ensembles of classifiers
Information Sciences: an International Journal
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In recent years, a series of manifold learning algorithms have been proposed for nonlinear dimensionality reduction. Most of them can run in a batch mode for a set of given data points, but lack a mechanism to deal with new data points. Here we propose an extension approach, i.e., mapping new data points into the previously learned manifold. The core idea of our approach is to propagate the known coordinates to each of the new data points. We first formulate this task as a quadratic programming, and then develop an iterative algorithm for coordinate propagation. Tangent space projection and smooth splines are used to yield an initial coordinate for each new data point, according to their local geometrical relations. Experimental results and applications to camera direction estimation and face pose estimation illustrate the validity of our approach.