Under such circumstances, this paper presents a novel greedy algorithm for sparse unmixing of hyperspectral data.This algorithm has low computational complexity of getting an approximate solution for the l0 problem directly and can exploit the joint sparsity among all the pixels in the hyperspectral ...
展开 关键词: greedy algorithms image processing source separation video signal processing forward backward greedy algorithm image processing low rank component multitask learning signal demixing source separation problem 会议名称: 2014 48th Asilomar Conference on Signals, Systems and Computers 会议...
Nowak, "A greedy forward-backward algorithm for atomic norm constrained minimization," in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013, pp. 5885-5889.N. Rao, P. Shah, S. Wright, and R. Nowak, "A greedy forward-backward algorithm for ...
T. Zhang, "Adaptive forward-backward greedy algorithm for learn- ing sparse representations," IEEE Trans. Inf. Theory, vol. 57, no. 7, pp. 4689-4708, Jul. 2011.Zhang, T. ( 2011a ) Adaptive forward-backward greedy algorithm for learning sparse representations . IEEE Trans. Inform. Theor...
Forward-backward pursuit (FBP) algorithm is a novel two-stage greedy approach. However once its forward and backward steps were determined during iteration, it would make computing time increased and affected the reconstruction efficiency. This paper presents a algorithm called forward-backward pursuit ...
Forward-backward searchGreedy algorithmsThe Forward–Backward Pursuit (FBP), which is a recently proposed method, receives wide attention due to the high reconstruction accuracy. In this paper, we use the fusion strategy and propose the Fusi
As a novel two-stage greedy approximation algorithm, Forward-Backward Pursuit (FBP) algorithm attracts wide attention because of its high reconstruction accuracy and no need for sparsity as a priori information. However, the FBP algorithm has to spend much more time to get a higher accuracy. In...
Therefore, instead of finding the best transmit candidate by evaluating all complete paths through the trellis, a greedy shortest path algorithm =-=[9, 10]-=- can approximately solve the hard detection problem in this section. In this greedy algorithm, we prune the incoming paths at each ...