Xia J.-M.,Electronic Engineering Institute |
Xia J.-M.,Key Laboratory of Electronic Restriction of Anhui |
Yang J.-A.,Electronic Engineering Institute |
Yang J.-A.,Key Laboratory of Electronic Restriction of Anhui |
And 2 more authors.
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | Year: 2013
The conventional active learning methods have one of the following defects: needing some labeled data selected randomly, ignoring the detail of the data structure, or requiring the fixed scale of the neighborhood to be set in advance. Therefore, a learning algorithm, active learning based on sparse linear reconstruction (SLR), is proposed based on the sparse representation model and the optimum experimental design method. Firstly, the sparse representation method is utilized to obtain the sparse reconstruction matrix. Then, the selection is realized with constraining the sparse reconstructive relationship among each data point and optimizing the reconstruction performance. Theory analysis and simulation results demonstrate that the proposed method selects the appropriate data points without any related prior information and does not need the fixed range between the nearby fields. Meanwhile, compared with the traditional methods such as neighborhood entropy, transductive experimental design and locally linear reconstruction, the proposed algorithm has better performance. Source