A novel algorithm for solving the small sample size problem in face recognition is proposed.
提出了一种在人脸识别中解决小样本问题的新算法。
Experiments demonstrate that the proposed method can effectively solve the small sample size problem of LDA.
多个人脸数据库上的实验结果表明,本算法能够有效地解决线性判别分析中的小样本规模问题。
In ORL face database, the experimental results prove that the algorithm outperforms traditional methods in small sample size problem.
在OR L人脸库上的实验结果说明,该算法对小样本数据的识别具有明显优势。
Through maximalizing the margin, we can obtain the optimal projection vector, and avoid the small sample size problem due to singularity of the within-class scatter.
通过极大化该边界获得最优投影向量,同时避免因类内离散度矩阵奇异导致的小样本问题。
Through maximalizing the margin, we can obtain the optimal projection vector, and avoid the small sample size problem due to singularity of the within-class scatter.
通过极大化该边界获得最优投影向量,同时避免因类内离散度矩阵奇异导致的小样本问题。
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