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_neighborsint, default=5
number of neighbors to consider for each point.
- n_componentsint, default=2
number of coordinates for the manifold
- regfloat, default=1e-3
regularization constant, multiplies the trace of the local covariance matrix of the distances.
- eigen_solver{‘auto’, ‘arpack’, ‘dense’}, default=’auto’
auto : algorithm will attempt to choose the best method for input data
- arpackuse 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.
- denseuse 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.
- tolfloat, default=1e-6
Tolerance for ‘arpack’ method Not used if eigen_solver==’dense’.
- max_iterint, default=100
maximum number of iterations for the arpack solver. Not used if eigen_solver==’dense’.
- method{‘standard’, ‘hessian’, ‘modified’, ‘ltsa’}, default=’standard’
standard
: use the standard locally linear embedding algorithm. see reference [1]hessian
: use the Hessian eigenmap method. This method requiresn_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_tolfloat, default=1e-4
Tolerance for Hessian eigenmapping method. Only used if
method == 'hessian'
- modified_tolfloat, default=1e-12
Tolerance for modified LLE method. Only used if
method == 'modified'
- neighbors_algorithm{‘auto’, ‘brute’, ‘kd_tree’, ‘ball_tree’}, default=’auto’
algorithm to use for nearest neighbors search, passed to neighbors.NearestNeighbors instance
- random_stateint, RandomState instance, default=None
Determines the random number generator when
eigen_solver
== ‘arpack’. Pass an int for reproducible results across multiple function calls. See :term:Glossary <random_state>
.- n_jobsint or None, default=None
The number of parallel jobs to run.
None
means 1 unless in ajoblib.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
- 1
Roweis, S. & Saul, L. Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323 (2000).
- 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).
- 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
- 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
(X[, y])Compute the embedding vectors for data X
fit_transform
(X[, y])Compute the embedding vectors for data X and transform X.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Transform new points into embedding space.
-
fit
(X, y=None)[source]¶ Compute the embedding vectors for data X
- Parameters
- Xarray-like of shape [n_samples, n_features]
training set.
- yIgnored
- Returns
- selfreturns an instance of self.
-
fit_transform
(X, y=None)[source]¶ Compute the embedding vectors for data X and transform X.
- Parameters
- Xarray-like of shape [n_samples, n_features]
training set.
- yIgnored
- Returns
- X_newarray-like, shape (n_samples, n_components)
-
get_params
(deep=True)[source]¶ Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
-
set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.
-
transform
(X)[source]¶ Transform new points into embedding space.
- Parameters
- Xarray-like of shape (n_samples, n_features)
- Returns
- X_newarray, shape = [n_samples, n_components]
Notes
Because of scaling performed by this method, it is discouraged to use it together with methods that are not scale-invariant (like SVMs)