Rank features for unsupervised learning using Laplacian scores
ranks features (variables) in idx
= fsulaplacian(X
)X
using the Laplacian scores. The
function returns idx
, which contains the indices of features ordered by
feature importance. You can use idx
to select important features for
unsupervised learning.
specifies additional options using one or more name-value pair arguments. For example, you
can specify idx
= fsulaplacian(X
,Name,Value
)'NumNeighbors',10
to create a similarity graph using 10 nearest neighbors.
[1] He, X., D. Cai, and P. Niyogi. "Laplacian Score for Feature Selection." NIPS Proceedings. 2005.