[1]SHAO Dongheng,YANG Wenyuan,ZHAO Hong.Label distribution learning based on k-means algorithm[J].CAAI Transactions on Intelligent Systems,2017,12(3):325-332.[doi:10.11992/tis.201704024]
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Label distribution learning based on k-means algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 3 Page number: 325-332 Column: 学术论文—智能系统 Public date: 2017-06-25
Title:
Label distribution learning based on k-means algorithm
Author(s):
SHAO Dongheng;  YANG Wenyuan;  ZHAO Hong
Lab of Granular Computing, Minnan Normal University, Zhangzhou 363000, China
Keywords:
label distribution; clustering; k-means; Minkowski distance; multi-label; weight matrix; mean vector; softmax function
CLC:
TP181
DOI:
10.11992/tis.201704024
Abstract:
Label distribution learning is a new type of machine learning paradigm that has emerged in recent years. It can solve the problem wherein different relevant labels have different importance. Existing label distribution learning algorithms adopt the parameter model with conditional probability, but they do not adequately exploit the relation between features and labels. In this study, the k-means clustering algorithm, a type of prototype-based clustering, was used to cluster the training set instance since samples having similar features have similar label distribution. Hence, a new algorithm known as label distribution learning based on k-means algorithm (LDLKM) was proposed. It firstly calculated each cluster’s mean vector using the k-means algorithm. Then, it got the mean vector of the label distribution corresponding to the training set. Finally, the distance between the mean vectors of the test set and the training set was applied to predict label distribution of the test set as a weight. Experiments were conducted on six public data sets and then compared with three existing label distribution learning algorithms for five types of evaluation measures. The experimental results demonstrate the effectiveness of the proposed KM-LDL algorithm.
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