图 书 馆
利用扭曲高斯过程的函数分解Function Factorization Using Warped Gaussian Processes |
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| 课程网址: | http://videolectures.net/icml09_schmidt_ffuw/ |
| 主讲教师: | Mikkel N. Schmidt |
| 开课单位: | 剑桥大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  我们引入了一种称为函数分解的非线性回归的新方法，该方法适用于输出变量可以通过输入的非线性函数之间的许多乘法交互项合理建模的问题。该想法是通过较低维子空间上的较简单函数的乘积之和来近似高维空间上的复杂函数。函数分解可以看作是矩阵和张量分解方法的推广，其中数据通过向量的外积之和来近似。我们提出了一种非参数贝叶斯方法来进行函数分解，其中分解函数的先验是扭曲的高斯过程，我们使用哈密顿马尔可夫链蒙特卡罗进行推理。与使用PARAFAC和GEMANOVA模型的高斯过程回归和张量分解相比，我们证明了该方法在食品科学数据集上的卓越预测性能。 |
| 课程简介: | We introduce a new approach to non-linear regression called function factorization, that is suitable for problems where an output variable can reasonably be modeled by a number of multiplicative interaction terms between non-linear functions of the inputs. The idea is to approximate a complicated function on a high-dimensional space by the sum of products of simpler functions on lower-dimensional subspaces. Function factorization can be seen as a generalization of matrix and tensor factorization methods, in which the data are approximated by the sum of outer products of vectors. We present a non-parametric Bayesian approach to function factorization where the priors over the factorizing functions are warped Gaussian processes, and we do inference using Hamiltonian Markov chain Monte Carlo. We demonstrate the superior predictive performance of the method on a food science data set compared to Gaussian process regression and tensor factorization using PARAFAC and GEMANOVA models. |
| 关 键 词: | 函数分解; 非线性回归; 乘法交互项 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-24：lxf |
| 阅读次数: | 131 |