A Java implementation of linear models for regression and classification of in-memory data sets. I needed to get myself reacquainted with Java, which I last used two years ago, for one of my graduate classes, so I figured this would be the perfect way to refresh myself while doing something fun at the same time.

Although I may not be able to make significant progress during the school year, I do intend to finish this project since it will be a nice exercise for me to refresh my knowledge on some linear supervised learning methods.

Inspired by the scikit-learn SGDClassifier.

Some loss functions I wish to make available.

Least-squares

Supports l₁, l₂, and naive elastic-net regularization. See Optimization methods for solving details.

Logistic

Gives a logistic regression classifier and also supports l₁, l₂, and naive elastic-net regularization.

Hinge

Gives a linear support vector classifier and supports all previously mentioned regularization methods.

Huber

We take δ = 1 in this case. Supports all previously mentioned regularization methods.

A list of the optimization methods I intend to implement.

Batch [sub]gradient descent

Will support l₂ regularization and l₁ + naive elastic net regularization using iterative soft thresholding¹. For those who are interested, there is a derivation of the soft thresholding operator from the promixal mapping in the first answer to this question on the mathematics StackExchange.

Stochastic [sub]gradient descent

Will also support l₁, l₂, and naive elastic net regularization using iterative soft thresholding.

These both will directly solve the primal formulation of the problem by operating on the loss functional directly. Not sure if I plan to implement any methods to solve the dual problem.