Parallel Lasso for Large-Scale Video Concept Detection
Bo Ting Geng, Yangxi Li, Dacheng Tao, Meng Wang, Zheng-Jun Zha, Chao Xu · IEEE Transactions on Multimedia · 2011
Existing video concept detectors are generally built upon the kernel based machine learning techniques, e.g., support vector machines, regularized least squares, and logistic regression, just to name a few. However, in order to build robust detectors, the learning process suffers from the scalability issues including the high-dimensional multi-modality visual features and the large-scale keyframe examples. In this paper, we propose parallel lasso (Plasso) by introducing the parallel distributed computation to significantly improve the scalability of lasso (thel1regularized least squares). We apply the parallel incomplete Cholesky factorization to approximate the covariance statistics in the preprocess step, and the parallel primal-dual interior-point method with the Sherman-Morrison-Woodbury formula to optimize the model parameters. For a dataset withnsamples in ad-dimensional space, compared with lasso, Plasso significantly reduces complexities from the originalO(d3) for computational time andO(d2) for storage space toO(h2d/m) andO(hd/m) , respectively, if the system hasmprocessors and the reduced dimensionhis much smaller than the original dimensiond. Furthermore, we develop the kernel extension of the proposed linear algorithm with the sample reweighting schema, and we can achieve similar time and space complexity improvements [time complexity fromO(n3) toO(h2n/m) and the space complexity fromO(n2) toO(hn/m), for a dataset withntraining examples]. Experimental results on TRECVID video concept detection challenges suggest that the proposed method can obtain significant time and space savings for training effective detectors with limited communication overhead.