IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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DOI：
10.1002/0471667196.ess1206
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发表时间：
2004-10
期刊：
影响因子：
--
通讯作者：
King-Sun Fu
中科院分区：
文献类型：
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作者：
King-Sun Fu
在基于外观的对象识别范例的上下文中，通常认为基于LDA（线性判别分析）的算法上级基于PCA（主成分分析）的算法。在这次交流中，我们表明情况并非总是如此。我们首先通过使用直观上似乎合理的论点，然后通过在人脸数据库上显示实际结果来展示我们的案例（cid：12）。我们的总体结论是，当训练数据集很小时，PCA可以优于LDA，并且PCA对不同的训练数据集不太敏感。
In the context of the appearance-based paradigm for object recognition, it is generally believed that algorithms based on LDA (Linear Discriminant Analysis) are superior to those based on PCA (Principal Components Analysis). In this communication we show that this is not always the case. We present our case (cid:12)rst by using intuitively plausible arguments and then by showing actual results on a face database. Our overall conclusion is that when the training dataset is small, PCA can outperform LDA, and also that PCA is less sensitive to di(cid:11)erent training datasets.