## 4 Methods for EIT Data Reduction and CNN Model Compression for Efficient Edge Deployment  
### Techniques for EIT Data Reduction  
The general principle of operation of these algorithms is as follows:  
* **Principal Component Analysis**: PCA is a statistical technique designed to transform complex, high-dimensional datasets into more concise representations while preserving the most important information [25]. By systematically normalising data to zero mean and unit variance, PCA first computes a covariance matrix that highlights the relationships between variables. Through eigenvector and eigenvalue decomposition, the method identifies principal components--orthogonal axes that capture the most significant patterns of variation in the data. These principal components are ranked according to their ability to explain the variance in the data, with the first component capturing the most variability, followed by subsequent components that account for progressively less variation.
* **Chi-square test**: The Chi-square test [26] is a robust statistical technique designed to evaluate the presence of significant associations between variables by systematically comparing observed and expected frequencies. This probabilistic method constructs a contingency table that captures the actual distribution of data points and compares it to a hypothetical scenario of variable independence. By calculating a Chi-squared statistic that quantifies the magnitude of the deviation between the observed and expected frequencies, the test generates a \(p\)-value that represents the probability of obtaining such a result by chance. If the \(p\)-value falls below a predetermined significance threshold (typically 0.05), the null hypothesis of independence will be rejected, indicating a statistically meaningful relationship between the variables.
* **Minimum Redundancy Maximum Relevance**: The Minimum Redundancy Maximum Relevance (mRMR) algorithm is an intelligent feature selection technique designed to reduce complex, high-dimensional datasets to their most informative and unique components. By systematically evaluating the relevance of individual features to a target variable while minimising redundancy among selected features, mRMR provides a sophisticated approach to dimensionality reduction in machine learning [27]. The algorithm strategically prioritises features that provide maximum predictive power and minimum information overlap, effectively identifying a subset of variables that capture the most essential and independent aspects of the target data. This nuanced selection process is particularly important in applications with large feature spaces and complex predictive modelling, where traditional feature selection methods may fail to capture the non-redundant information embedded in the dataset.
* **The Laplacian Score**: The Laplacian Score is a feature selection technique that aims to preserve the intrinsic geometric structure of complex datasets [28] by identifying features that preserve local neighbourhood relationships. By constructing a sophisticated similarity graph in which data points are connected based on their inherent proximity and relationships, the algorithm evaluates each feature's ability to preserve the local topology of the underlying data manifold. This approach goes beyond traditional feature selection methods by focusing on preserving the intrinsic data geometry, effectively identifying features that capture the most meaningful and locally representative information. Features with lower Laplacian scores are considered to be the most informative, as they best preserve the subtle structural nuances and proximity relationships within the dataset. This method is particularly powerful in high-dimensional spaces, where traditional feature selection techniques may overlook the complex, non-linear relationships between data points.