Spectral Embedding

[aamijar]

Resolves #6912

Benchmarks

Dataset	n_samples	n_features	sklearn_time (s)	cuml_time (s)	Speedup
deep-image-96	50000	96	87.027506	0.037730	2306.607180x
fashion-mnist-784	50000	784	2.912207	0.053853	54.076849x
gist-960	50000	960	73.453647	0.046808	1569.253974x
glove-25	50000	25	114.452536	0.031595	3622.489824x
sift-128	50000	128	14.886346	0.039108	380.651352x

Usage

from sklearn import datasets
from cuml.manifold import SpectralEmbedding
import cupy as cp

# (1500, 3) -> (1500, 2)
n_samples = 1500
S_points, S_color = datasets.make_s_curve(n_samples, random_state=0)

spectral = SpectralEmbedding(n_components=2, n_neighbors=None, random_state=42)
embedding = spectral.fit_transform(cp.asarray(S_points, order='C', dtype=cp.float32))


from sklearn.datasets import fetch_openml

# (70000, 784) -> (70000, 2)
mnist = fetch_openml('mnist_784', version=1)
X, y = mnist.data, mnist.target.astype(int)

spectral = SpectralEmbedding(n_components=2, n_neighbors=None, random_state=42)
embedding = spectral.fit_transform(cp.asarray(X, order='C', dtype=cp.float32))

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[cjnolet]

See comments in review

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