# Optimizing IoT Video Data: Dimensionality Reduction for Efficient Deep Learning on Edge Computing
## 2 Related Work
Semi-supervised approaches leverage the strengths of both supervised and unsupervised learning by combining unsupervised pre-training with labeled data to improve generalization. For instance, pre-training with unlabeled data helps the model learn meaningful representations, while fine-tuning with labeled data refines its accuracy. This combination is particularly valuable when labeled data are scarce, enhancing the model's ability to generalize effectively. Overall, deep learning-based dimensionality reduction methods can handle the complexity and non-linearity of high-dimensionality data [33],providing efficient and meaningful feature representations for tasks like image, video, and sensor data analysis. Dimensionality reduction is crucial for handling video data due to their high computational complexity and memory requirements and the challenge of extracting effective spatio-temporal features. Unlike static data, videos involve a temporal dimension, which requires specialized approaches for the efficient management of both spatial and temporal aspects [12]. Various methods have been developed to reduce dimensionality, making video data more manageable while maintaining the quality of extracted features. Techniques like the Spatio-temporal Prompting Network (STPN) [34], Regularized Deep Neural Networks (rDNNs) [35], and lightweight optimization for frame interpolation [36] exemplify how dimensionality reduction can enhance performance in video classification, detection, and segmentation tasks. These methods focus on reducing redundancy, preserving essential information, and improving computational feasibility. Dimensionality reduction also finds applications beyond video classification, such as human action recognition, medical video analysis, and human detection. For action recognition, local CNN features are aggregated to create global representations, addressing GPU memory limitations while preserving performance [37]. In medical video analysis, particularly gastrointestinal endoscopy, hybrid feature extraction techniques reduce computational costs while retaining critical diagnostic information [38, 39]. Additionally, incremental Principal Component Analysis (PCA) has been used to improve human detection by reducing the dimensionality of CoHOG features [40]. Overall, dimensionality reduction plays an essential role in making video data analysis practical and improving efficiency, memory management, and accuracy across various video-related applications. In the study of animal behavior [41, 42, 43] and, more specifically, in bird-related research [44, 45, 46, 47], videos serve as a primary data structure for analysis and insight generation. However, to the best of our knowledge, no prior work has applied dimensionality reduction techniques to this specific domain, making our approach the first to explore and integrate these methods for analyzing video data in the context of animal behavior and avian studies. When considering datasets for training deep learning algorithms, there is a notable scarcity of resources focused on bird behavior recognition. For instance, the VB100 dataset [48], which consists of video recordings of birds in their natural environments, is annotated only with bird species and lacks labels for the actions being performed. Consequently, this dataset is not suitable for behavior recognition tasks. Similarly, the AnimalKingdom dataset [49] contains video recordings of various animals and includes annotations of their corresponding actions. However, as this dataset encompasses a wide range of animal species, including but not limited to birds, it is not suitable for focused bird behavior recognition. The Visual WetlandBirds Dataset [11] provides video recordings exclusively of birds, making it the most appropriate dataset for this study.