Acceleration of Subspace Learning Machine via Particle Swarm Optimization and Parallel Processing
This work addresses efficiency improvements for users of SLM in classification and regression tasks, but it is incremental as it optimizes an existing method.
The paper tackled the high computational complexity of the subspace learning machine (SLM) by accelerating it using particle swarm optimization (PSO) and parallel processing, achieving a speed-up factor of 577 in training time while maintaining comparable performance.
Built upon the decision tree (DT) classification and regression idea, the subspace learning machine (SLM) has been recently proposed to offer higher performance in general classification and regression tasks. Its performance improvement is reached at the expense of higher computational complexity. In this work, we investigate two ways to accelerate SLM. First, we adopt the particle swarm optimization (PSO) algorithm to speed up the search of a discriminant dimension that is expressed as a linear combination of current dimensions. The search of optimal weights in the linear combination is computationally heavy. It is accomplished by probabilistic search in original SLM. The acceleration of SLM by PSO requires 10-20 times fewer iterations. Second, we leverage parallel processing in the SLM implementation. Experimental results show that the accelerated SLM method achieves a speed up factor of 577 in training time while maintaining comparable classification/regression performance of original SLM.