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25 March 2023 A study on selecting the optimal number of features for clustering without label information
Mengbo You, Kouichi Konno
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Proceedings Volume 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023; 125921T (2023) https://doi.org/10.1117/12.2665403
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2023, 2023, Jeju, Korea, Republic of
Abstract
Most studies on high-dimensional data preprocessing for data mining and pattern recognition, attention have focused on unsupervised feature selection, which aims to reduce data redundancy and select the most representative features from massive unlabelled data. Previous work on convex nonnegative matrix factorization with adaptive graph constraint has indicated that selecting a few essential features helps replace the original features for clustering. However, the problem exists in how to decide the quantity of these essential features without prior label information. This study proposes a customized evaluation criterion to evaluate each essential feature set. A significant advantages of this method is that the optimal number of features can be determined without knowing their true labels. Experiments were verified by the consistency between the results of our method without label information and that of the comparison method, which required label information.
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Mengbo You and Kouichi Konno "A study on selecting the optimal number of features for clustering without label information", Proc. SPIE 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023, 125921T (25 March 2023); https://doi.org/10.1117/12.2665403
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KEYWORDS
Feature selection
Data mining
Machine learning
Mathematical optimization
Pattern recognition
Prior knowledge
Statistical analysis
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