Please use this identifier to cite or link to this item:
Title: Parallel lasso for large-scale video concept detection
Authors: Geng, B.
Li, Y.
Tao, D.
Wang, M.
Zha, Z.-J. 
Xu, C.
Keywords: Incomplete cholosky factorization
parallel learning
video concept detection
Issue Date: 2012
Citation: Geng, B., Li, Y., Tao, D., Wang, M., Zha, Z.-J., Xu, C. (2012). Parallel lasso for large-scale video concept detection. IEEE Transactions on Multimedia 14 (1) : 55-65. ScholarBank@NUS Repository.
Abstract: 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 (the regularized 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 with samples in a -dimensional space, compared with lasso, Plasso significantly reduces complexities from the original for computational time and for storage space to and respectively, if the system has $m$ processors and the reduced dimension is much smaller than the original dimension. 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 from to and the space complexity from to for a dataset with training 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. © 2006 IEEE.
Source Title: IEEE Transactions on Multimedia
ISSN: 15209210
DOI: 10.1109/TMM.2011.2174781
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Mar 21, 2019


checked on Mar 12, 2019

Page view(s)

checked on Feb 9, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.