Nonlinear Dimensionality Reduction Techniques and Their Applications
نویسنده
چکیده
Dimensionality reduction is the search for a small set of variables to describe a large set of observed dimensions. Some benefits of dimensionality reduction include data visualization, compact representation, and decreased processing time. In this paper, we review two nonlinear techniques for dimensionality reduction: Isometric Feature Mapping (Isomap) and Locally Linear Embedding (LLE), and apply the algorithms to pose estimation. We find that the nonlinear techniques produce results with a higher accuracy rate than Eigenspace, a linear algorithm based on Principal Component Analysis (PCA).
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تاریخ انتشار 2004