图 书 馆
附近组件分析和度量学习Neighbourhood Components Analysis and Metric Learning |
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| 课程网址: | http://videolectures.net/lce06_roweis_ncaml/ |
| 主讲教师: | Sam Roweis |
| 开课单位: | 无 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  说您要进行K最近邻分类。除了选择K外，还必须选择一个距离函数，以定义“最近”。我将讨论一种从数据本身中学习的方法，该方法将用于KNN分类。学习算法邻域成分分析（NCA）直接最大化训练集上遗忘的KNN分数的随机变量。当然，最终的分类模型是非参数的，不对类分布的形状或它们之间的边界做任何假设。我还将讨论该方法的一种变体，它是Fisher判别式的概括，它通过尝试将同一个类中的所有示例折叠到一个点，然后尝试将其他类中的示例无限远地推开来定义凸优化问题。通过用低秩矩阵近似度量，这些学习算法也可以用于获取原始输入特征的低维线性嵌入，从而可以用于数据可视化和高维的快速分类。 |
| 课程简介: | Say you want to do K-Nearest Neighbour classification. Besides selecting K, you also have to chose a distance function, in order to define ”nearest”. I’ll talk about a method for learning – from the data itself – a distance measure to be used in KNN classification. The learning algorithm, Neighbourhood Components Analysis (NCA) directly maximizes a stochastic variant of the leave-one-out KNN score on the training set. Of course, the resulting classification model is non-parametric, making no assumptions about the shape of the class distributions or the boundaries between them. I will also discuss an variant of the method which is a generalization of Fisher’s discriminant and defines a convex optimization problem by trying to collapse all examples in the same class to a single point and trying to push examples in other classes infinitely far away. By approximating the metric with a low rank matrix, these learning algorithms, can also be used to obtain a low-dimensional linear embedding of the original input features allowing that can be used for data visualization and very fast classification in high dimensions. |
| 关 键 词: | K最近邻分类; KNN分类; NCA; 领域成分分析; Fisher判别式 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-04-01：zhouxj |
| 最后编审: | 2020-05-25：cxin |
| 阅读次数: | 37 |