keynote:lesson03
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| 不适用于非高斯噪声污染的数据集。\\ | 不适用于非高斯噪声污染的数据集。\\ | ||
| 设数据$m\times n$矩阵$M=L+S$，$L$为潜在的低秩矩阵，$S$噪声矩阵。如果$S$非高斯噪声，例如稀疏且幅值不定的噪声，那么PCA将失效。\\ | 设数据$m\times n$矩阵$M=L+S$，$L$为潜在的低秩矩阵，$S$噪声矩阵。如果$S$非高斯噪声，例如稀疏且幅值不定的噪声，那么PCA将失效。\\ | ||
| - | {{:keynote:pca_done_well.jpg|playground}}\\ | + | {{:keynote:pca_done_well.jpg|}}\\ |
| {{:keynote:pca_fail.jpg|}}\\ | {{:keynote:pca_fail.jpg|}}\\ | ||
| 此时，宜用改进的模型RPCA(Robust PCA)。 | 此时，宜用改进的模型RPCA(Robust PCA)。 | ||
keynote/lesson03.txt · Last modified: 2023/08/19 21:02 (external edit)
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