Multi-view clustering has garnered significant attention due to its ability to explore shared information from multiple views. Applications of multi-view clustering include image and video analysis, bioinformatics, and social network analysis, in which integrating diverse data sources enhances data understanding and insights. However, existing multi-view models suffer from the following limitations: (1) directly extracting latent representations from raw data using encoders is susceptible to interference from noise and other factors and (2) complementary information among different views is often overlooked, resulting in the loss of crucial unique information from each view. Therefore, we propose a distinctive double-level deep multi-view collaborative learning approach. Our method further processes the latent representations learned by the encoder through multiple layers of perceptrons to obtain richer semantic information. In addition, we introduce dual-path guidance at both the feature and label levels to facilitate the learning of complementary information across different views. Furthermore, we introduce pre-clustering methods to guide mutual learning among different views through pseudo-labels. Experimental results on four image datasets (Caltech-5V, STL10, Cifar10, Cifar100) demonstrate that our method achieves state-of-the-art clustering performance, evaluated using standard metrics, including accuracy, normalized mutual information, and purity. We compare our proposed method with existing clustering algorithms to validate its effectiveness.
Chinese text classification has been in the research stage, there are many machine learning algorithms that can be used, such as logical regression, SVM, KNN, naive Bayes, random forest, neural network and so on. In this paper, taking Chinese modern novels as an example, we use various algorithms for classification and comparison, and choose the best algorithm for naive Bayes and neural network. After adjusting the TF-IDF algorithm and processing the participle according to the TF-IDF value, the accuracy of classification is improved obviously. The logistic regression with the lowest accuracy can increase about 6.7%,while the simple Bias and neural network can reach 100%.
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