图 书 馆
傅立叶域中的聚类排名分析Clustering Rankings in the Fourier Domain |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_gaudel_clustering/ |
| 主讲教师: | Romaric Gaudel |
| 开课单位: | 法国国家信息与自动化研究所 |
| 开课时间: | 2011-10-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | It is the purpose of this paper to introduce a novel approach to clustering rank data on a set of possibly large cardinality n ∈ ℕ*, relying upon Fourier representation of functions defined on the symmetric group . In the present setup, covering a wide variety of practical situations, rank data are viewed as distributions on . Cluster analysis aims at segmenting data into homogeneous subgroups, hopefully very dissimilar in a certain sense. Whereas considering dissimilarity measures/distances between distributions on the non commutative group , in a coordinate manner by viewing it as embedded in the set [0,1] n! for instance, hardly yields interpretable results and leads to face obvious computational issues, evaluating the closeness of groups of permutations in the Fourier domain may be much easier in contrast. Indeed, in a wide variety of situations, a few well-chosen Fourier (matrix) coefficients may permit to approximate efficiently two distributions on as well as their degree of dissimilarity, while describing global properties in an interpretable fashion. Following in the footsteps of recent advances in automatic feature selection in the context of unsupervised learning, we propose to cast the task of clustering rankings in terms of optimization of a criterion that can be expressed in the Fourier domain in a simple manner. The effectiveness of the method proposed is illustrated by numerical experiments based on artificial and real data. |
| 关 键 词: | 傅里叶; 数据聚类; 聚类排序 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-08：cxin |
| 阅读次数: | 97 |