图 书 馆
最大与最小：张量分解和ICA与近线性样本复杂度Max vs Min: Tensor Decomposition and ICA with nearly Linear Sample Complexity |
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| 课程网址: | https://videolectures.net/videos/colt2015_kyng_sample_complexity |
| 主讲教师: | Rasmus J. Kyng |
| 开课单位: | 信息不详。欢迎您在右侧留言补充。 |
| 开课时间: | 2025-02-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present a simple, general technique for reducing the sample complexity of matrix and tensor decomposition algorithms applied to distributions. We use the technique to give a polynomial-time algorithm for standard ICA with sample complexity nearly linear in the dimension, thereby improving substantially on previous bounds. The analysis is based on properties of random polynomials, namely the spacings of an ensemble of polynomials. Our technique also applies to other applications of tensor decompositions, including spherical Gaussian mixture models. |
| 关 键 词: | 降低矩阵; 张量分解算法：样本复杂性 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-03-28：zsp |
| 最后编审: | 2025-03-28：zsp |
| 阅读次数: | 5 |