Machine Learning: Locally Linear Embedding

This work goes into the field of machine learning. So far most learning techniques needed supervision and used linear models to represent the data. The learning algorithm evaluated in this work is unsupervised and of non-linear nature. It was first proposed in 2000, but has so far only been evaluated by computer science and mathematics researchers. During my work I focused on some implementation issues important to engineers. Furthermore some new incremental formulations were proposed.

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