Performing nonlinear dimension reduction with Local Linear Embedding

Locally linear embedding (LLE) is an extension of PCA, which reduces data that lies on a manifold embedded in a high dimensional space into a low dimensional space. In contrast to ISOMAP, which is a global approach for nonlinear dimension reduction, LLE is a local approach that employs a linear combination of the k-nearest neighbor to preserve local properties of data. In this recipe, we will give a short introduction of how to use LLE on an s-curve data.

Getting ready

In this recipe, we will use digit data from lle_scurve_data within the lle package as our input source.

How to do it...

Perform the following steps to perform nonlinear dimension reduction with LLE:

  1. First, you need ...

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