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- W2609491836 abstract "Visualization helps us to understand single-label and multi-label classification problems. In this paper, we show several standard techniques for simultaneous visualization of samples, features and multi-classes on the basis of linear regression and matrix factorization. The experiment with two real-life multi-label datasets showed that such techniques are effective to know how labels are correlated to each other and how features are related to labels in a given multi-label classification problem." @default.
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- W2609491836 date "2016-12-01" @default.
- W2609491836 modified "2023-09-23" @default.
- W2609491836 title "Simultaneous visualization of samples, features and multi-labels" @default.
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- W2609491836 doi "https://doi.org/10.1109/icpr.2016.7900193" @default.
- W2609491836 hasPublicationYear "2016" @default.
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