# Research and Application of Semi-Supervised Category Dictionary Model Based on Transfer Learning
###### Abstract  
Classification tasks are pivotal across diverse applications, yet the burgeoning amount of data, coupled with complicating factors such as noise, exacerbates the challenge of classifying complex data. For algorithms that require a large amount of data, the annotation work for datasets is also exceptionally complex and tedious. Drawing upon existing research, this paper first introduces a novel semi-supervised category dictionary model based on transfer learning (SSDT). This model is designed to construct a more representative category dictionary and to delineate the associations of information across different domains, utilizing the lens of conditional probability distribution. This approach is particularly apt for semi-supervised transfer learning scenarios. Subsequently, the proposed method is applied to the domain of bearing fault diagnosis. This model is suitable for transfer scenarios; moreover, its semi-supervised characteristic eliminates the need for labeling the entire input dataset, significantly reducing manual workload. Experimental results attest to the model's practical utility. When benchmarked against other 6 models, the SSDT model demonstrates enhanced generalization performance. dictionary model; transfer learning; classification task; complex data +
Footnote †: journal: Medical  
coding (STCS) model [5]. This model, built upon the TSC framework, unifies dictionary updating, sparse coefficient resolution, and classifier training within its objective function, thereby effectively addressing the issues of transfer learning and classifier training inherent in the TSC approach. Compared to unsupervised transfer learning, semi-supervised transfer learning offers the advantage of integrating multiple strategies to minimize differences among similar samples across different domains [5; 6]. Semi-supervised models can effectively leverage a combination of labeled and unlabeled data, which reduces the need for manual labeling efforts [7]. By utilizing both labeled and unlabeled data, semi-supervised models can learn more about the underlying structure of the data and achieve better generalization performance than supervised models that use only labeled data. It also can be more robust to noise in the data, since it relies on the inherent structure of the data rather than solely on the labels. This approach tends to yield fewer adverse effects during the transfer process. However, research in semi-supervised transfer learning scenarios remains nascent and underserved. For instance, the literature [8] (pp. 1192-1198) does not optimally utilize the labeled sample category information, and the aspects of classifier training and cross-domain migration within the model are treated as separate entities. While the supervised transfer sparse coding (STCS) model acknowledges the semi-supervised transfer scenario, it neither deliberately designs the model for this context, nor effectively employs the labeled information within the target domain [9] (pp. 1187-1192). Based on above research status, a novel semi-supervised dictionary learning model based on transfer learning (SSDT) is proposed in this paper. The model framework incorporates an augmentation strategy that updates the data matrix--composed of source domain tokens, target domain tokens, and pseudo-tagged samples--in each iteration, thereby generating a more representative series of sparse features from the category dictionary. Concurrently, the model constructs sub-objective function terms from the vantage point of spatial distance and conditional probability distribution of samples within a category across domains, thereby rendering it highly applicable in semi-supervised transfer scenarios [10] (pp. 2066-2073). The proposed methodology judiciously incorporates target domain samples in each iteration to construct transfer sub-objective function terms. This strategic approach mitigates the potential adverse transfer effect that might be triggered by an over-reliance on pseudo-tagged samples from the target domain.