Spectral Synergetic Network for Image Classification
In order to reduce the relativity and improve the separability of prototype pattern vectors, a spectralbased synergetic network learning algorithm is proposed in this paper. The most attractive feature of the new method is that its complexity is linear with data dimension. To approximate the optimal cut and prevent instability due to information loss, all eigenvectors are used. The eigenvalues and eigenvectors of its affinity matrix provide global information about its structure. In order to determine kernel parameter, cross validation is applied. Experiments on IRIS dataset, Brodatz textural images and SAR bridges show that the new algorithm is effective.
Xiuli Ma Wanggen Wan Rui Wang
School of Communication and Information Engineering Shanghai University, Shanghai 200072, China
国际会议
上海
英文
1714-1718
2010-10-20（万方平台首次上网日期，不代表论文的发表时间）