Title: Exploiting a new probabilistic model: S2FA - Simple-Supervised Factor Analysis
2019 summer, FII, UAIC, Iasi, Romania
The implementation of the R package can be found here.
Abstract:
In machine learning, a classic supervised-unsupervised pair of algorithms is Gaussian Joint Bayes-Expectation Maximization (EM) for Gaussian Mixture Model. Starting from this, one can create an algorithm for semi-supervised learning and also other extensions. In this work, we started from an existing unsupervised learning algorithm (EM for Factor Analysis), created its supervised version (S2) and exploited the properties of this new model. Two main extensions of S2 are: the semi-supervised (S3) case and the missing data (MS3) case. All the algorithms are derived starting from the (log-)likelihood of the (observed) data and using the maximum likelihood principle. The S2 algorithms are trained using closed-form formulas, while S3 and MS3 are trained using the EM algorithm. The S2 and S3 algorithms can also be vectorised.
Apart from the derivation of the algorithms, we found two significant links between the supervised counterpart of (unconstrained) Factor Analysis and (multi-output) Linear Regression, and we present them here. Some experiments are also included.