https://scholars.lib.ntu.edu.tw/handle/123456789/498959
標題: | 2D sparse dictionary learning via tensor decomposition | 作者: | Hsieh, S.-H. Lu, C.-S. SOO-CHANG PEI |
關鍵字: | CANDECOMP/PARAFAC (CP) decomposition; Dictionary learning; Sparse representation; Tensor | 公開日期: | 2014 | 起(迄)頁: | 492-496 | 來源出版物: | 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 | 摘要: | The existing dictionary learning methods mostly focus on ID signals, leading to the disadvantage of incurring overload of memory and computation if the size of training samples is large enough. Recently, 2D dictionary learning paradigm has been validated to save massive memory usage, especially for large-scale problems. To address this issue, we propose novel 2D dictionary learning algorithms based on tensors in this paper. Our learning problem is efficiently solved by CANDECOMP/PARAFAC (CP) decomposition. In addition, our algorithms guarantee sparsity constraint, which makes that sparse representation of the learned dictionary is equivalent to the ground truth. Experimental results confirm the effectness of our methods. © 2014 IEEE. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/498959 | DOI: | 10.1109/GlobalSIP.2014.7032166 | SDG/關鍵字: | Information science; Tensors; CANDECOMP/PARAFAC; Dictionary learning; Dictionary learning algorithms; Large-scale problem; Learned dictionaries; Sparse representation; Sparsity constraints; Tensor decomposition; Learning algorithms |
顯示於: | 電機工程學系 |
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