smile.manifold
Class LLE
- java.lang.Object
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- smile.manifold.LLE
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public class LLE extends java.lang.ObjectLocally 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.
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Constructor Summary
Constructors Constructor and Description LLE(double[][] data, int d, int k)Constructor.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method and Description double[][]getCoordinates()Returns the coordinates of projected data.int[]getIndex()Returns the original sample index.GraphgetNearestNeighborGraph()Returns the nearest neighbor graph.
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Constructor Detail
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LLE
public LLE(double[][] data, int d, int k)Constructor.- Parameters:
data- the dataset.d- the dimension of the manifold.k- k-nearest neighbor.
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Method Detail
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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.
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getCoordinates
public double[][] getCoordinates()
Returns the coordinates of projected data.
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getNearestNeighborGraph
public Graph getNearestNeighborGraph()
Returns the nearest neighbor graph.
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