Nonlinear Dimensionality Reduction – Locally Linear Embedding versus Isomap
نویسنده
چکیده
Real data of natural and social sciences is often very high-dimensional. However, the underlying structure can in many cases be described by a small number of features. Recently two new nonlinear methods for reducing the dimensionality of such data, Locally Linear Embedding and Isomap, have been suggested and successfully applied. This report compares both algorithms by means of several synthetic and real data sets. The results not only give insight into the behaviour of both methods when facing various data, but also provide a basis for choosing the adequate method for real-world problems.
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