Is clustering still limited to mixture model hacks?<p>Hi:</p>
<p> I'd like to use infer.net and I need to investigate change points and anomalies in temporal, well-correlated-high-dim, semi-big-data w/o labels. I was thinking of trying some recommended techniques in hiearchical subspace
clustering as well as topic model variants. I was going to try the dynamic topic model of Blei-Lafferty, but it uses logistic normal in place of Dirichlet, so I can't even use LDA to hack an impl. Though, I suppose
the Markov Topic Model of Wang, Thiesson, Meek, Blei could leverage LDA. Or for that matter, just leverage straight LDA into a subspace model. Now for more ordinary clustering, I was thinking of graph spectral and mean-shift, but I don't have
a clue how to leverage infer.net? Of course, concurrently, I'll also play around with the usual dimension reduction techniques.
</p>
<p> Assume I have strong motivation to try to have infer.net solve this problem.
</p>
<p> I've already tried global LDA w/o success. I do know of several R packages that would really help at this point and perhaps the quickest route would be to solve the problem using R or other tools and then replicate on infer.net.
But the point of this exercise is to only use infer.net. ;-)</p>
<p> What would you do? (I can give you any information you need, except the data.)</p>
<p>Thx -- Paul </p>© 2009 Microsoft Corporation. All rights reserved.Tue, 01 May 2012 00:46:33 Z1cbcb7d1-385b-41b2-a6a8-e07450314d00- https://social.microsoft.com/Forums/en-US/1cbcb7d1-385b-41b2-a6a8-e07450314d00/is-clustering-still-limited-to-mixture-model-hacks?forum=infer.net#1cbcb7d1-385b-41b2-a6a8-e07450314d00https://social.microsoft.com/Forums/en-US/1cbcb7d1-385b-41b2-a6a8-e07450314d00/is-clustering-still-limited-to-mixture-model-hacks?forum=infer.net#1cbcb7d1-385b-41b2-a6a8-e07450314d00paulring2https://social.microsoft.com:443/profile/paulring2/?type=forumIs clustering still limited to mixture model hacks?<p>Hi:</p>
<p> I'd like to use infer.net and I need to investigate change points and anomalies in temporal, well-correlated-high-dim, semi-big-data w/o labels. I was thinking of trying some recommended techniques in hiearchical subspace
clustering as well as topic model variants. I was going to try the dynamic topic model of Blei-Lafferty, but it uses logistic normal in place of Dirichlet, so I can't even use LDA to hack an impl. Though, I suppose
the Markov Topic Model of Wang, Thiesson, Meek, Blei could leverage LDA. Or for that matter, just leverage straight LDA into a subspace model. Now for more ordinary clustering, I was thinking of graph spectral and mean-shift, but I don't have
a clue how to leverage infer.net? Of course, concurrently, I'll also play around with the usual dimension reduction techniques.
</p>
<p> Assume I have strong motivation to try to have infer.net solve this problem.
</p>
<p> I've already tried global LDA w/o success. I do know of several R packages that would really help at this point and perhaps the quickest route would be to solve the problem using R or other tools and then replicate on infer.net.
But the point of this exercise is to only use infer.net. ;-)</p>
<p> What would you do? (I can give you any information you need, except the data.)</p>
<p>Thx -- Paul </p>Tue, 01 May 2012 00:46:33 Z2012-05-01T00:46:33Z