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Relevance Vector Machine (RVM) #1513
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I'd have to read up on it again but in general I think RVMs would be a good addition. Gotta grab my Bishop. |
What is the relation between ARD and RVM? Is RVM just the "basis function" version of ARD? |
Btw is anyone ever bothered by the fact that the section |
Ok so we should use the |
I wonder whether there is a similar method for ARD? That would be cool as the current ARD implementation is quite slow :-/ |
No the implementation of RVM definitely does not use SMO. I think SMO is only used for SVM optimization. |
Bishop is "Machine Learning and Pattern Recognition". The algorithm is probably not completely easy to get right. If you want to start on this, it will definitely be a bigger project. If you are interested and want to implement it any way, go ahead. Implementing it is also quite a bit different from getting it into scikit-learn. That also involves writing tests, documentation and a user guide - and pretty code. For a first try you should definitely use just numpy. It uses blas internally and is therefore quite fast. |
OK for Cython and numpy. I didn't know bishop talk about RVM. |
ARD is also explained in Bishops book and in the user guide. It puts a diagonal Gaussian prior on the weights and tries to estimate the variances, which (as I understand it) is the same that RVM does. Is that correct? |
I realize that the ref mentioned : http://books.nips.cc/papers/files/nips20/NIPS2007_0976.pdf is not the implementation we use. I think that @vmichel implemented the |
Thanks @agramfort, I was wondering about that. I didn't go through the details but I thought as the paper was the only reference... I would very much appreciate it if you could add a comment there citing the chapter of bishop that was used an maybe saying we should implement the other paper instead. |
(btw of of the slowest things in the test suite right now is fitting ARD on the boston dataset in the common tests) |
see : #1530 |
It does: logistic regressions. |
I believe that the most promising rapid solver of ARD is to implement the |
I put on a gist a code wrote a while ago. If somebody wants to work on ARD I thought it could be useful. https://gist.github.com/4494613 WARNING : it's not much tested and I don't guarantee correctness but it |
@amueller |
That sounds odd. If the objective is the same, you should be able to use the same methods for optimization, right? |
Yes for sure you should, but I guess the authors of RVM used a different optimization strategy to have a faster and more sparse algorithm. |
@yedtoss I'm pretty sure there is some other difference. As I said before, this might be that RVM work in a feature space or with a kernel or something. Otherwise you could just replace the ARD implementation? That is regression, though, and you want classification, right? @agramfort do you know anything about the difference of ARD and RVM? |
@amueller |
I guess I'd have to read the papers to know whats going on.... |
sorry guys I am not much of a Bayesian guy... I don't know well the |
RVMs are patented by Microsoft. |
crazy. |
@larsmans @amueller while there is a patent in the US for RVM, the author recommends a GPLv2 Matlab implementation on his web page, so I guess it is ok to implement it... Best, |
@kalxas License and patent are quite orthogonal and GPLv2 in particular didn't address software patents. The rights you have with such an implementation are the intersection of the rights granted by the GPL and those granted by the patent holder. That said, I found out in the meantime that support vector machines are patented by AT&T but the patent was apparently never enforced. If something similar can be proven of RVMs, I might change my mind about them. |
@larsmans I wrote a pure numpy/python port of dlib implementation (awfully slow at the moment, I'll try to cythonize it). According to the header, dlib's implem has been around since 2008 and they seem fine with it. Would you consider changing your mind about having RVM in sklearn ? |
Let's hear @GaelVaroquaux's opinion on this. The dlib implem doesn't show a thing as long as you can't prove it's widely used without a patent license. |
OK,thanks On Mon, Oct 19, 2015 at 8:29 AM, Gael Varoquaux notifications@github.com
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Couldnt we implement this as very lightweight class based on our new Gaussian Process implementation? As a far as I understand, RVR is only the name given to a GP with a special kind of kernel. Though this would require only minimal effort, basing the implementation of RVR on the one of GP may not be the most appropriate thing to do? CC: @jmetzen |
@amueller @GaelVaroquaux @ZaixuCui @yedtoss @jlopezpena Hi, I implemented fast version of Relevance Vector Machine with scikit-learn API, Code: https://github.com/AmazaspShumik/sklearn_bayes/blob/master/sklearn_bayes/rvm/fast_rvm.py There are four implemented classes in the code: So may be RegressionARD and ClassificationARD can be useful as well |
@AmazaspShumik thank you so much for your implementation. Great work 👍 |
Thank you for your efforts very much. Wish you all the best. Zaixu |
Has anyone had trouble implementing @AmazaspShumik predict_proba method? |
any one here have library for RVM on php? i dont understand with RVm can explain for me? |
any one have library RVM for PHP? |
Patent will be expired soon 2019-09-04 |
Some pointed in discussion links to @AmazaspShumik implementation are broken, just put them here for people who interested in (and some other implementations): https://github.com/AmazaspShumik/sklearn_bayes - RVM + some other algs implementation Also, here is relevant papers collection: |
There is a C++ implementation of RVM in there (supplementary material of a paper): |
Microsoft patent has expired. Could we possibly add it to sklearn? |
It easily clears the standard requirements, so I don't see why not. Maybe having some good/convincing examples might be interesting. |
On Murphy's book he claims the RVMs performance is really similar to SVMs but has the advantage of being a true probabilistic method so it gives calibrated probabilities as answers. Here https://github.com/probml/pmtk3/blob/master/docs/tutorial/html/tutKernelClassif.html he compared the methods using a small dataset |
Can you provide a link to the rendered version? |
I will try to work on the implementation. Any help would be highly appreciated. |
IIRC, one advantage of RVM over SVM, is that you can find the optimal C parameter without doing an optimization pass. |
Hi @amueller and everybody! We saw this thread and decided to implement a sklearn-compatible version of the RVM (https://github.com/Mind-the-Pineapple/sklearn-rvm). We based a lot of what we did on the JamesRitchie implementation. It would be great if someone would be willing to take a look at it and feedbacks and contributions (@themrzmaster) are welcome :) |
Hi @PedroFerreiradaCosta |
Hi @mustuner ! Thank you for trying out our API! Best, |
Thank you @PedroFerreiradaCosta Let me try that |
Since some side compatible packages are available, we can close this issue. |
RVM is a Bayesian framework for obtaining sparse solutions to regression and classification tasks. It used a model of identical form to SVM ( Support Vector Machine). It solves the following disadvantages of SVM:
-- The number of basis function in SVM grows linearly with the size of the training set
In RVM, we start with 0 basis and incrementally update (add/delete) the set of basis function until convergence.
-- SVM predictions are not probabilistic while RVM's are probabilistic
-- It is necessary in SVM to estimate the margin trade-off parameter 'C' which is not the case in RVM
-- SVM kernel should be positive definite. In RVM we can use any kernel.
It is already implemented in dlib http://dlib.net/dlib/svm/rvm_abstract.h.html and there is also a matlab implementation here http://www.vectoranomaly.com/downloads/downloads.htm. These codes should serve as a guide.
I think it will be a good idea to add it to scikit-learn.
References :
1-
Tipping, M. E. and A. C. Faul (2003). Fast marginal likelihood maximisation for sparse Bayesian models. In C. M. Bishop and B. J. Frey (Eds.), Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, Jan 3-6.
2-
Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1, 211–244.
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