Esthetic preference mining of Chinese typefaces using multiview cluster analysis

Abstract

Previous neuroesthetic studies have proved that Chinese typefaces can be viewed as an esthetic preference stimulus by observing differences in event related potential (ERP) waves among three preferences, namely, like, dislike, and neutral. We first reconfirm this conclusion by introducing a multiview clustering method of kernelized tensor singular value decomposition (KT-SVD) to construct an esthetic preference recognition model based on electroencephalograms (EEGs). Our approach regards data from different frequency bands as different views describing the esthetic preferences of Chinese fonts, explore the relevance of all view features through the constraint of tensor multi-rank minimization, and obtains the esthetic preferences using the clustering results. Additionally, the input-perturbation correlation method is used to correlate the amplitude of the electrodes with different types of esthetic preferences and describe the relationship between the key frequency-band combinations and electrodes, and take out the electrodes most relevant to likes, dislikes, and neutrality, including 3 electrodes of Top-1, 6 electrodes of Top-2, 9 electrodes of Top-3, and 12 electrodes of Top-4, forming four different combinations of EEG features for esthetic preference recognition experiments. Experimental results show that the method based on multiview clustering can solve the correlation analysis of neural signals and esthetic preferences and mine the electrodes most relevant to the esthetic preferences of fonts.