图 书 馆
独立成分分析Independent Component Analysis |
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| 课程网址: | http://videolectures.net/mlss05au_hyvarinen_ica/ |
| 主讲教师: | Aapo Hyvärinen |
| 开课单位: | 赫尔辛基大学 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In independent component analysis (ICA), the purpose is to linearly decompose a multidimensional data vector into components that are as statistically independent as possible. For nongaussian random vectors, this decomposition is not equivalent to decorrelation as is done by principal component analysis, but something considerably more sophisticated. ICA allows one to separate nongaussian source signals from their linear mixtures 'blindly', i.e. using no other information than the congaussianity of the source signals. ICA can also be used to extract features from image and sound signals according to the principle of redundancy reduction that has its origins in the neurosciences. In my talks I will review the basic theory and theoretical background of ICA together with some recent theoretical developments. |
| 关 键 词: | 独立分量分析; 多维数据; 非高斯随机向量 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-07：王勇彬(课程编辑志愿者) |
| 阅读次数: | 204 |