Documentation of 'smile.manifold.LLE' Java class
LLE
smile.manifold

Class LLE



  • public class LLE
    extends java.lang.Object
    Locally Linear Embedding. It has several advantages over Isomap, including faster optimization when implemented to take advantage of sparse matrix algorithms, and better results with many problems. LLE also begins by finding a set of the nearest neighbors of each point. It then computes a set of weights for each point that best describe the point as a linear combination of its neighbors. Finally, it uses an eigenvector-based optimization technique to find the low-dimensional embedding of points, such that each point is still described with the same linear combination of its neighbors. LLE tends to handle non-uniform sample densities poorly because there is no fixed unit to prevent the weights from drifting as various regions differ in sample densities.
    See Also:
    IsoMap,

    References

    1. Sam T. Roweis and Lawrence K. Saul. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290(5500):2323-2326, 2000.
    • Constructor Summary

      Constructors 
      Constructor and Description
      LLE(double[][] data, int d, int k)
      Constructor.
    • Constructor Detail

      • LLE

        public LLE(double[][] data,
                   int d,
                   int k)
        Constructor.
        Parameters:
        data - the dataset.
        d - the dimension of the manifold.
        k - k-nearest neighbor.
    • Method Detail

      • getIndex

        public int[] getIndex()
        Returns the original sample index. Because LLE is applied to the largest connected component of k-nearest neighbor graph, we record the the original indices of samples in the largest component.
      • getCoordinates

        public double[][] getCoordinates()
        Returns the coordinates of projected data.
      • getNearestNeighborGraph

        public Graph getNearestNeighborGraph()
        Returns the nearest neighbor graph.

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