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您的检索式：作者名="Chen Caikou"
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| 1 | Nearest-neighbor classifier motivated marginal discriminant projections for face recognition显示文摘边缘的菲希尔分析(MFA ) 是为脸识别的一个代表性的基于边缘的学习算法。在 MFA 的一个主要问题是怎么选择适当参数, k 1 并且 k 2,构造各自内在并且惩罚图。在这份报纸,我们建议一个新奇方法打电话近邻居(NN ) 分类器激发了边缘的判别式设计(NN-MDP ) 。由 NN 分类器激发了, NN-MDP 寻求一些设计向量阻止数据样品错误地被分类。象 MFA 一样, NN-MDP 能同时描绘样品的紧密和可分性。而且与 MFA 相对照,没有未知参数, NN-MDP 能活跃地构造内在的图和惩罚图。ORL,耶鲁,和 FERET 脸数据库上的试验性的结果显示出那 NN-MDP 不仅避免邻居参数选择的难驾驭,和高开销,而且也是更适用的比另外的方法面对识别 NN 分类器。 | Pu HUANG Zhenmin TANG Caikou CHEN Xintian CHENG | 2011 | Frontiers of Computer Science2011，5，4： | 3 |
| 2 | Local Maximal Margin Discriminant Embedding for Face Recognition显示文摘 | Huang Pu Tang Zhenmin Chen Caikou | 2014 | Journal of Visual Communication&Image Representation2014，25，2： | 1 |
| 3 | LOCAL CORRELATION DISCRIMINANT ANALYSIS AND ITS SEMI-SUPERVISED EXTENSION显示文摘Considering limitations of Linear Discriminant Analysis (LDA) and Marginal Fisher Analysis (MFA), a novel discriminant analysis called Local Correlation Discriminant Analysis (LCDA) is proposed in this paper. The main idea behind LCDA is to use more robust similarity measure, correlation metric, to measure the local similarity between image data. This results in better classifi-cation performance. In addition, to further improve the discriminant power of LCDA, we extend LCDA to semi-supervised case, which can make use of both labeled and unlabeled data to perform dis-criminant analysis. Extensive experimental results on ORL and AR face databases demonstrate that the proposed LCDA and its semi-supervised version are superior to Principal Component Analysis (PCA), LDA, CEA, and MFA. | Chen Caikou Shi Jun | 2011 | Journal of Electronics(China)2011，28，3： | 1 |
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