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. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
De Ridder, D., Kouropteva, O., Okun, O., Pietikäinen, M., & Duin, R. P. W. (2003). Supervised locally linear embedding. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2714, 333–341. https://doi.org/10.1007/3-540-44989-2_40
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