Abstract
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.
The financial support of the Infotech Oulu graduate school is gratefully acknowledged. This work was partly sponsored by the Dutch Foundation for Applied Sciences (STW) under project number AIF.4997.
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de Ridder, D., Kouropteva, O., Okun, O., Pietikäinen, M., Duin, R.P.W. (2003). Supervised Locally Linear Embedding. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_40
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DOI: https://doi.org/10.1007/3-540-44989-2_40
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