# Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals
## 2 Materials and Methods
### LFS-Based Sentiment Classification Algorithm for Audovisual Scenes
#### 2.2.5 Selection of Eigenvalues  
For a specific learning algorithm, not all features are beneficial for algorithm learning, such as irrelevant features and redundant features. In practical applications, an excessive number of features can easily lead to the problem of dimensionality curse. Feature selection through feature parameter screening can not only greatly reduce the learning and training time cost of the model but also significantly increase the interpretability of the model. The screening procedure of this experiment consists of ANOVA screening, homoscedastic screening by means of kernel density estimation (KDE) plots, and correlation screening by means of correlation heat maps. The specific steps are detailed as follows. 1. ANOVA screening. Variance analysis is performed on the selected eigenvalues. The divergence of the features is observed, and if the variance of a certain feature is close to 0, it indicates that the sample dataset has little variability on this feature and the classification effect of this feature on the samples is small. Therefore, this redundant feature is removed. In this experiment, eigenvalues with a variance less than 0.1 are removed, and after variance analysis, the eigenvalues are normalized. 2. Homoscedastic screening by means of KDE plots. KDE plots are used to infer the distribution of population data based on a limited sample. The result of kernel density estimation is the estimated probability density function of the samples, and based on this result, the clustering areas of data can be obtained [38]. KDE is a nonparametric estimation that does not incorporate any prior knowledge, but fits the distribution based on the characteristics and properties of the dataset itself, and thus obtains a more  
Figure 7: CNN classification model. optimized model than parametric estimation methods [39]. In this experiment, the differences in data distribution between eigenvalues in the training set and the test set are evaluated by drawing kernel density estimation plots of the dataset distribution. If the overlap of the kernel density estimation plots is high, it indicates that the data distribution differences are small, and the feature is retained [40]; otherwise, it is removed. Figure 8 presents eigenvalues with small data distribution differences, while Figure 9 presents eigenvalues with large data distribution differences. (3) Correlation screening by means of correlation heat maps. By analyzing the correlation between two or more variables, the closeness of the correlation can be measured. Figure 10 shows the correlation heat map of SW mono eigenvalues with labels under 9 s window length segmentation. In this experiment, the Spearman rank correlation coefficient is used to analyze the data, which is a nonparametric measure of rank correlation. The mathematical formula for calculating the Spearman rank correlation coefficient is as follows:  
Figure 8: The KDE plots of the above feature parameter values in the training and test sets overlap well. This indicates that there is little difference between the data distribution of this feature parameter value on the training and test sets, and so the feature is retained. Figure 9: The KDE plots of the above feature parameter values on the training and test sets have low overlap. It means that the feature parameter value shows a large difference between the data distribution on the training and test sets, and so the feature is rejected. \[\rho=\frac{\sum_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{\sqrt{\sum_{i =1}^{n}{(x_{i}-\overline{x})^{2}}{\sum_{i=1}^{n}{(y_{i}-\overline{y})^{2}}}}}\]  
The larger the obtained correlation coefficient, the stronger the linear impact of these feature variables on the target variable [41]. Based on the experimental results, the final selection of eigenvalues was determined by filtering out those with a correlation coefficient with the target variable greater than 0.1.