Efficient Low-Rank Semidefinite Programming with Robust Loss Functions
This addresses robustness against outliers for machine learning applications using low-rank SDP, but it is incremental as it adapts existing methods to new loss functions.
The paper tackled the problem of improving robustness in low-rank semidefinite programming for machine learning by replacing square loss with robust losses like ℓ1-loss, resulting in an efficient algorithm that outperforms state-of-the-art methods in experiments.
In real-world applications, it is important for machine learning algorithms to be robust against data outliers or corruptions. In this paper, we focus on improving the robustness of a large class of learning algorithms that are formulated as low-rank semi-definite programming (SDP) problems. Traditional formulations use square loss, which is notorious for being sensitive to outliers. We propose to replace this with more robust noise models, including the $\ell_1$-loss and other nonconvex losses. However, the resultant optimization problem becomes difficult as the objective is no longer convex or smooth. To alleviate this problem, we design an efficient algorithm based on majorization-minimization. The crux is on constructing a good optimization surrogate, and we show that this surrogate can be efficiently obtained by the alternating direction method of multipliers (ADMM). By properly monitoring ADMM's convergence, the proposed algorithm is empirically efficient and also theoretically guaranteed to converge to a critical point. Extensive experiments are performed on four machine learning applications using both synthetic and real-world data sets. Results show that the proposed algorithm is not only fast but also has better performance than the state-of-the-art.