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。

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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。

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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。

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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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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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