To be clear, LLE's weights are found via a linear solution involving
covariances of local neighborhoods, which can be constructed from a
matrix of pairwise distances in a way analogous to that of metric MDS.
Jake
Matthieu Brucher wrote:
> Hi,
>
> I think some of the algorithms already offer t
Hi,
I think some of the algorithms already offer this (Laplacian Eigenmaps for
instance).
I'm -1 for LLE as LLE does not compute distances, but weights based on the
points directly.
Matthieu
2011/9/21 Jacob VanderPlas
> Hello,
> I recently was contacted by someone interested in using manifold
On Wed, Sep 21, 2011 at 8:56 AM, Robert Layton wrote:
> I do really like the metric='precomputed' concept, which allows both
> implementing actual metrics (euclidean, manhattan) as well as passing a
> precomputed array in. If the algorithm doesn't allow it for whatever reason*
> throw an error. T
On 21 September 2011 09:50, Gael Varoquaux wrote:
> On Tue, Sep 20, 2011 at 04:25:58PM -0700, Jacob VanderPlas wrote:
> > I recently was contacted by someone interested in using manifold
> > learning methods on abstract metric spaces: that is, the training data
> > is a matrix of pairwise distance
On Tue, Sep 20, 2011 at 04:25:58PM -0700, Jacob VanderPlas wrote:
> I recently was contacted by someone interested in using manifold
> learning methods on abstract metric spaces: that is, the training data
> is a matrix of pairwise distances rather than a set of points. It would
> be fairly str
On 21 September 2011 09:25, Jacob VanderPlas <
vanderp...@astro.washington.edu> wrote:
> Hello,
> I recently was contacted by someone interested in using manifold
> learning methods on abstract metric spaces: that is, the training data
> is a matrix of pairwise distances rather than a set of point
Hello,
I recently was contacted by someone interested in using manifold
learning methods on abstract metric spaces: that is, the training data
is a matrix of pairwise distances rather than a set of points. It would
be fairly straightforward to implement this for basic LLE and Isomap,
and could