基于改进极限学习机算法的行为识别

重点研究了极限学习机ELM对行为识别检测的效果。针对在线学习和行为分类上存在计算复杂性和时间消耗大的问题,提出了一种新的行为识别学习算法(ELM-Cholesky)。该算法首先引入了基于Cholesky分解求ELM的方法,接着依据在线学习期间核函数矩阵的更新特点,将分块矩阵Cholesky分解算法用于ELM的在线求解,使三角因子矩阵实现在线更新,从而得出一种新的ELM-Cholesky在线学习算法。新算法充分利用了历史训练数据,降低了计算的复杂性,提高了行为识别的准确率。最后,在基准数据库上采用该算法进行了大量实验,实验结果表明了这种在线学习算法的有效性。

An action recognition algorithm based on improved extreme learning machine

We focus on detecting the efficiency of extreme learning machine (ELM) on action recognition. To overcome the problems of computational complexity and time consumption of online learning and action classification, we propose a new action recognition algorithm (ELM-Cholesky). Firstly, a method based on Cholesky decomposition to seek the calculation of ELM is introduced into the algorithm. Secondly, according to the characteristics of kernel function matrix updates during online learning, we utilize the partitioned Cholesky decomposition algorithm for online solution to ELM, which realizes online updating of the triangular factor matrix. Finally, we can obtain a new online learning algorithm, called ELM-Cholesky. The new algorithm can make full use of historical training data, reduce the complexity of calculation, and improve action identification accuracy. Moreover, extensive experiments on benchmark database verify the effectiveness of this online learning algorithm.