Linear and Nonlinear Data Dimensionality Reduction

  title={Linear and Nonlinear Data Dimensionality Reduction},
  author={David Gering},
This report discusses one paper for linear data dimensionality reduction, Eigenfaces, and two recently developed nonlinear techniques. The first nonlinear method, Locally Linear Embedding (LLE), maps the input data points to a single global coordinate system of lower dimension in a manner that preserves the relationships between neighboring points. The second method, Isomap, computes geodesic distances along a manifold as sequences of hops between neighboring points, and then applies… CONTINUE READING