Minimum Redundancy Maximum Relevance Feature Selection

To implement Minimum Redundancy Maximum Relevance (mRMR) feature selection for images, we can follow these sequential steps with corresponding code implementation approaches. First, we need to collect and prepare source data files. This typically involves loading image datasets along with their corresponding labels or attributes using functions like imread() for image loading and categorical() for label encoding in MATLAB. Next, we apply feature selection algorithms to identify features with the highest relevance to image characteristics. The mRMR algorithm can be implemented using mutual information calculations between features and target variables, while minimizing redundancy among selected features through correlation analysis. Key functions may include computing mutual information matrices and feature ranking based on mRMR criteria. Then, we perform maximum relevance feature selection by prioritizing features that show the strongest correlation with target characteristics. This can be achieved through statistical measures like Pearson correlation or information-theoretic approaches, potentially using functions such as corr() for correlation analysis or custom mutual information computations. Finally, we optimize the selected features according to both minimum redundancy and maximum relevance principles. This optimization phase may involve removing redundant features using clustering techniques or forward/backward selection algorithms, and potentially adding complementary features through iterative refinement processes. The implementation might include feature subset evaluation using cross-validation and performance metrics calculation. In summary, the mRMR feature selection method provides an effective framework for extracting and selecting image features, leading to improved performance in image analysis and processing tasks through mathematically-grounded feature optimization.