The ppca
packages implements different inference methods for Probabilistic Principal Component Analysis described by Christopher Bishop.
Examples files are located in notebooks
:
- BPPCA: Evaluation of Bayesian inference for Probabilistic PCA
Probabilistic Principal Component Analysis
License: GNU General Public License v2.0
The ppca
packages implements different inference methods for Probabilistic Principal Component Analysis described by Christopher Bishop.
Examples files are located in notebooks
:
I've looked through your code and the paper Variational principal components.
And I found you compute exception such as <W.T.dot(W)> by tr(q.w_mean).dot(q.w_mean).
However, for gaussian distribution, there is <x.dot(x.T)> = cov + mu.dot(mu.T). So I think you should add the cov term?
Am I right?
I am doing the same job right row, but I found the variational lower bound decrease sometimes, hope that you can have a try. ^_^
Hi, having a quick look at the code, I think this version of ppca does not handle missing data. Am I wrong here? Will this code be able to deal with missing data?
Thanks,
Lijing
In ppca.__fit_ml you are writing
Line 94 in 8b9a0f2
which probably corresponds with
in Bishop, Tipping.
In other words, you are calculating eigenvalue by squaring singular value.
Is this correct? Shouldn't it be divided by number of samples also?
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.