一种约减支持向量域描述算法RSVDD

Reduced support vector domain description method RSVDD

为加快支持向量域描述(SVDD)的训练速度,提出基于约减集的约简支持向量域描述算法RSVDD.由于描述边界仅由支持向量决定,且支持向量多分布在描述边缘附近,该算法采用每个样本到中心的距离作为支持向量的一种可能性度量,选取距离较大的部分样本作为约减集参与SVDD训练.人造数据和基准集数据上的仿真实验表明了RSVDD的有效性和优越性,保证了目标类和奇异值类的分类精度,缩减了训练规模和训练时间.

To accelerate the training of SVDD, a support vector domain description method RSVDD based on reduced sets is proposed. Since the boundary is determined by a small portion of data called support vectors which distribute around the description boundaryl the proposed algorithm treats the distance to the center as a probability measure of support vectors for each sample, and selects the former ones ranking as the reduced sets to participate in the SVDD training. Simulations over artificial and benchmark data show its effectiveness and superiority： RSVDD reduces the training scale and the training time, while maintaining the accuracy of targets and outliers.