sklearn.manifold.locally_linear_embedding¶
- sklearn.manifold.locally_linear_embedding(X, n_neighbors, n_components, reg=0.001, eigen_solver='auto', tol=1e-06, max_iter=100, method='standard', hessian_tol=0.0001, modified_tol=1e-12, random_state=None)¶
Perform a Locally Linear Embedding analysis on the data.
Parameters: X : {array-like, sparse matrix, BallTree, KDTree, NearestNeighbors}
Sample data, shape = (n_samples, n_features), in the form of a numpy array, sparse array, precomputed tree, or NearestNeighbors object.
n_neighbors : integer
number of neighbors to consider for each point.
n_components : integer
number of coordinates for the manifold.
reg : float
regularization constant, multiplies the trace of the local covariance matrix of the distances.
eigen_solver : string, {‘auto’, ‘arpack’, ‘dense’}
auto : algorithm will attempt to choose the best method for input data
- arpack : use arnoldi iteration in shift-invert mode.
For this method, M may be a dense matrix, sparse matrix, or general linear operator. Warning: ARPACK can be unstable for some problems. It is best to try several random seeds in order to check results.
- dense : use standard dense matrix operations for the eigenvalue
decomposition. For this method, M must be an array or matrix type. This method should be avoided for large problems.
tol : float, optional
Tolerance for ‘arpack’ method Not used if eigen_solver==’dense’.
max_iter : integer
maximum number of iterations for the arpack solver.
method : {‘standard’, ‘hessian’, ‘modified’, ‘ltsa’}
- standard : use the standard locally linear embedding algorithm.
see reference [R43]
- hessian : use the Hessian eigenmap method. This method requires
n_neighbors > n_components * (1 + (n_components + 1) / 2. see reference [R44]
- modified : use the modified locally linear embedding algorithm.
see reference [R45]
- ltsa : use local tangent space alignment algorithm
see reference [R46]
hessian_tol : float, optional
Tolerance for Hessian eigenmapping method. Only used if method == ‘hessian’
modified_tol : float, optional
Tolerance for modified LLE method. Only used if method == ‘modified’
random_state: numpy.RandomState or int, optional :
The generator or seed used to determine the starting vector for arpack iterations. Defaults to numpy.random.
Returns: Y : array-like, shape [n_samples, n_components]
Embedding vectors.
squared_error : float
Reconstruction error for the embedding vectors. Equivalent to norm(Y - W Y, 'fro')**2, where W are the reconstruction weights.
References
[R43] (1, 2) Roweis, S. & Saul, L. Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323 (2000). [R44] (1, 2) Donoho, D. & Grimes, C. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proc Natl Acad Sci U S A. 100:5591 (2003). [R45] (1, 2) Zhang, Z. & Wang, J. MLLE: Modified Locally Linear Embedding Using Multiple Weights. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.70.382 [R46] (1, 2) Zhang, Z. & Zha, H. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. Journal of Shanghai Univ. 8:406 (2004)
