Computationally Efficient Hybrid Framework for MNIST Handwritten Digit Recognition
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Abstract
This research work suggests a combination classification technique for handwritten digit recognition based on MNIST dataset. The design embraces Principal Component Analysis (PCA) to reduce the level of dimensionality, KMeans clustering to get the pattern of the framework, and Random Forest to make the final prediction. By compressing the feature space into a 50 dimensional space and adding in cluster information, we are able to attain an accuracy of about 93.1 percent. The significant contributions of this paper are to prove the PCA-KMeans integration useful to improve classification, offering a computationally efficient solution as alternative to deep learning model, and to prove that feature augmentation using clustering outcomes leads to better discrimination of visually similar digits. The data suggest that the referred to hybrid model is stable and feasible approach to image identification and pattern recognition, which could be applied to more complicated dataset.
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