Supervised Locally Linear Embedding

Locally linear embedding (LLE) is a recently proposed method for unsupervised nonlinear dimensionality reduction. It has a number of attractive features: it does not require an iterative algorithm, and just a few parameters need to be set. Two extensions of LLE to supervised feature extraction were independently proposed by the authors of this paper. Here, both methods are unified in a common framework and applied to a number of benchmark data sets. Results show that they perform very well on high-dimensional data which exhibits a manifold structure.

• Type: Conference
• Year: 2003
• Where: ICANN
• Authors: Dick de Ridder, Olga Kouropteva, Oleg Okun, Matti Pietikäinen, Robert P. W. Duin
• Keywords: Benchmark Data Sets, ICANN 2003, Supervised Feature Extraction, Unsupervised Nonlinear Dimensionality