[1]WANG Yibin,LI Tianli,CHENG Yusheng.Label distribution learning based on spectral clustering[J].CAAI Transactions on Intelligent Systems,2019,14(5):966-973.[doi:10.11992/tis.201809019]
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Label distribution learning based on spectral clustering
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
14
Number of periods:
2019 5
Page number:
966-973
Column:
学术论文—机器学习
Public date:
2019-09-05
- Title:
- Label distribution learning based on spectral clustering
- Keywords:
- spectral clustering; label distribution learning; similarity matrix; Laplace transform; K-means; parametric model; label distribution; machine learning
- CLC:
- TP181
- DOI:
- 10.11992/tis.201809019
- Abstract:
- Label distribution is a new learning paradigm. Most of the existing algorithms use conditional probability to build parametric models but do not consider the links between samples fully, which increases computational complexity. On this basis, the spectral clustering algorithm is introduced to transform the clustering problem into the global optimum graph partitioning problem based on the similarity relation between samples. Thus, a label distribution learning algorithm combined with spectral clustering (SC-LDL) is proposed. First, we calculate the similarity matrix of the samples. Then, we transform the matrix using the Laplace transform to construct the feature vector space. Finally, we cluster the data to establish the parameter model with K-means algorithm and use this new model to predict the label distribution of unknown samples. The comparison between SC-LDL and the existing algorithm on multiple data sets shows that this algorithm is superior to multiple contrast algorithms. Furthermore, statistical hypothesis testing illustrates the effectiveness and superiority of the SC-LDL algorithm.
- References:
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