# Optimizing IoT Video Data: Dimensionality Reduction for Efficient Deep Learning on Edge Computing
## 2 Related Work  
Deep learning techniques excel at identifying and analyzing patterns in heterogeneous, high-dimensionality data. However, certain data modalities and scenarios benefit from reduced representations. For instance, video data present challenges due to their high spatial and temporal dimensionality. Similarly, in scenarios like the Internet of Things (IoT), where computational resources are limited and low inference times are critical, usingreduced data representations leads to smaller models with lower computational costs and faster inference times. In this context, dimensionality reduction involves mapping a set of input features to a smaller set while retaining meaningful information that can still be used for the same tasks as the original representation [12; 13; 14; 15]. Two main approaches are commonly discussed in the literature [12]: feature selection and feature extraction. Feature selection focuses on identifying and retaining a subset of the original features that preserve the most information. These features remain in their original form, and the methods prioritize selecting them based on specific criteria. This approach is particularly useful in scenarios with low-sample, high-dimensionality tabular data, where noise and feature redundancy are prevalent. In contrast, feature extraction transforms the initial features into a lower-dimensional representation, making it more suitable for unstructured data such as images and videos. Traditional linear approaches aim to achieve this transformation through linear methods. Examples include techniques based on variances and contribution ratios, such as Principal Component Analysis (PCA) [16; 17], Linear Discriminant Analysis (LDA) [18], and Factor Analysis (FA). Other linear feature extraction methods include Independent Component Analysis (ICA), Multi-Dimensional Scaling (MDS) [19], and Singular Value Decomposition (SVD) [20]. However, these linear methods are limited in their ability to address the inherent non-linearity and complexity of video data. Non-linear feature extraction algorithms provide flexibility to handle complex, non-linear relationships in data that linear methods fail to capture effectively. Examples include Kernel Principal Component Analysis (KPCA) [21], which uses kernel functions to project data into higher-dimensionality spaces where linear separation is feasible, and Locally Linear Embedding (LLE) [22], which maintains local neighborhood relationships while embedding data into a low-dimensionality space. Isometric Mapping (ISOMAP) [23] is another prominent approach, extending classical MDS by preserving geodesic distances between data points, effectively capturing the manifold structure of complex data. Non-negative Matrix Factorization (NMF) [24] offers a part-based representation of data by imposing non-negativity constraints, making it highly interpretable in fields such as text mining and facial recognition. These non-linear methods provide improved capability to represent the complexity and inherent non-linear nature of video and image data compared to traditional linear approaches. Dimensionality reduction using deep learning can be categorized into supervised [25], unsupervised, and semi-supervised methods. Supervised methods, such as Convolutional Neural Networks (CNNs) [26] and Transformer [27] models, effectively reduce feature dimensions by capturing both local and global features present in data. These methods work particularly well for structured tasks where labeled data are available, enabling the extraction of key information while minimizing irrelevant details. On the other hand, unsupervised methods, like Deep Autoencoders [28; 29; 30; 31] and Deep Belief Networks (DBNs) [32], focus on compressing high-dimensional data into lower-dimensional representations without relying on labels, making them suitable for discovering hidden structures and patterns in unstructured data.