邻里成分分析Neighbourhood Components Analysis |
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课程网址: | http://videolectures.net/mlss06tw_roweis_nca/ |
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: | 假设你想做K-最近邻分类。除了选择k,您还必须选择距离函数,以便定义";最近的";。我将讨论一种新的*学习*方法——从数据本身——一种用于KNN分类的距离测量。学习算法,邻域成分分析(NCA)直接最大化了训练集上遗漏一个KNN分数的随机变量。它还可以学习标记数据的低维线性嵌入,可用于数据可视化和高维中的快速分类。当然,生成的分类模型是非参数化的,没有对类分布的形状或它们之间的边界做任何假设。如果时间允许,我还将讨论在高斯核SVM分类器中学习相同距离度量的新工作。 |
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 novel 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. It can also learn a low-dimensional linear embedding of labeled data that can be used for data visualization and very fast classification in high dimensions. Of course, the resulting classification model is non-parametric, making no assumptions about the shape of the class distributions or the boundaries between them. If time permits, I'll also talk about newer work on learning the same kind of distance metric for use inside a Gaussian Kernel SVM classifier. |
关 键 词: | 距离函数; 邻里成分分析; 随机变异; 数据可视化; 支持向量机 |
课程来源: | 视频讲座网 |
最后编审: | 2020-06-26:cxin |
阅读次数: | 117 |