`sklearn.manifold`.LocallyLinearEmbedding¶

class sklearn.manifold.LocallyLinearEmbedding(n_neighbors=5, n_components=2, reg=0.001, eigen_solver=’auto’, tol=1e-06, max_iter=100, method=’standard’, hessian_tol=0.0001, modified_tol=1e-12, neighbors_algorithm=’auto’, random_state=None, n_jobs=None)[source]¶

Locally Linear Embedding

Read more in the User Guide.

Parameters:

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. Not used if eigen_solver==’dense’.

method : string (‘standard’, ‘hessian’, ‘modified’ or ‘ltsa’)

standard : use the standard locally linear embedding algorithm. see: reference [1]
hessian : use the Hessian eigenmap method. This method requires: n_neighbors > n_components * (1 + (n_components + 1) / 2 see reference [2]
modified : use the modified locally linear embedding algorithm.: see reference [3]
ltsa : use local tangent space alignment algorithm: see reference [4]

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'

neighbors_algorithm : string [‘auto’|’brute’|’kd_tree’|’ball_tree’]

algorithm to use for nearest neighbors search, passed to neighbors.NearestNeighbors instance

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. Used when eigen_solver == ‘arpack’.

n_jobs : int or None, optional (default=None)

The number of parallel jobs to run. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

Attributes:

embedding_ : array-like, shape [n_samples, n_components]: Stores the embedding vectors
reconstruction_error_ : float: Reconstruction error associated with embedding_
nbrs_ : NearestNeighbors object: Stores nearest neighbors instance, including BallTree or KDtree if applicable.

References

[R62e36dd1b056-1]

Roweis, S. & Saul, L. Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323 (2000).

[R62e36dd1b056-2]

Donoho, D. & Grimes, C. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proc Natl Acad Sci U S A. 100:5591 (2003).

[R62e36dd1b056-3]

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

[R62e36dd1b056-4]

Zhang, Z. & Zha, H. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. Journal of Shanghai Univ. 8:406 (2004)

Examples

>>> from sklearn.datasets import load_digits
>>> from sklearn.manifold import LocallyLinearEmbedding
>>> X, _ = load_digits(return_X_y=True)
>>> X.shape
(1797, 64)
>>> embedding = LocallyLinearEmbedding(n_components=2)
>>> X_transformed = embedding.fit_transform(X[:100])
>>> X_transformed.shape
(100, 2)

Methods

`fit`(self, X[, y])	Compute the embedding vectors for data X
`fit_transform`(self, X[, y])	Compute the embedding vectors for data X and transform X.
`get_params`(self[, deep])	Get parameters for this estimator.
`set_params`(self, \\params)	Set the parameters of this estimator.
`transform`(self, X)	Transform new points into embedding space.

__init__(self, n_neighbors=5, n_components=2, reg=0.001, eigen_solver=’auto’, tol=1e-06, max_iter=100, method=’standard’, hessian_tol=0.0001, modified_tol=1e-12, neighbors_algorithm=’auto’, random_state=None, n_jobs=None)[source]¶

fit(self, X, y=None)[source]¶

Compute the embedding vectors for data X

Parameters:	X : array-like of shape [n_samples, n_features] training set. y : Ignored
Returns:	self : returns an instance of self.

fit_transform(self, X, y=None)[source]¶

Compute the embedding vectors for data X and transform X.

Parameters:	X : array-like of shape [n_samples, n_features] training set. y : Ignored
Returns:	X_new : array-like, shape (n_samples, n_components)

get_params(self, deep=True)[source]¶

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

set_params(self, **params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.