Learning Sign-Constrained Support Vector Machines
This work provides new optimization algorithms for incorporating sign constraints into linear SVMs, which could benefit practitioners seeking to integrate domain knowledge into their models for improved generalization.
This paper explores incorporating sign constraints on weight coefficients in linear Support Vector Machines (SVMs) to leverage domain knowledge. It introduces two optimization algorithms, a projected gradient method and a Frank-Wolfe method, both with O(nd) computational cost per iteration and sublinear convergence.
Domain knowledge is useful to improve the generalization performance of learning machines. Sign constraints are a handy representation to combine domain knowledge with learning machine. In this paper, we consider constraining the signs of the weight coefficients in learning the linear support vector machine, and develop two optimization algorithms for minimizing the empirical risk under the sign constraints. One of the two algorithms is based on the projected gradient method, in which each iteration of the projected gradient method takes $O(nd)$ computational cost and the sublinear convergence of the objective error is guaranteed. The second algorithm is based on the Frank-Wolfe method that also converges sublinearly and possesses a clear termination criterion. We show that each iteration of the Frank-Wolfe also requires $O(nd)$ cost. Furthermore, we derive the explicit expression for the minimal iteration number to ensure an $ε$-accurate solution by analyzing the curvature of the objective function. Finally, we empirically demonstrate that the sign constraints are a promising technique when similarities to the training examples compose the feature vector.