Performance Analysis of SpectralNet Algorithm on Fashion MNIST and KMNIST Datasets: A Study on Clustering Efficiency and Resource Utilization
DOI:
https://doi.org/10.51846/vol8iss1pp47-54Keywords:
Deep Learning, SpectralNet, Clustering, Balanced Dataset, MNISTAbstract
Unsupervised learning faces an essential data clustering challenge in high dimensions where SpectralNet stands as an effective approach which combines deep learning with spectral clustering methods. A performance evaluation of SpectralNet measures its results on Fashion MNIST and KMNIST with accuracy as well as computational costs and resource demands under multiple hyperparameter configurations. The investigation examines how generalization changes with various distribution scenarios through the assessment of balanced versus unbalanced dataset splits. Higher embedding dimension values lead to superior clustering precision, whereas it demands increased processor capacity. The research reveals how SpectralNet handles accuracy-efficiency relationships in dataset analysis to demonstrate its practical capabilities for complex information.
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