Classifying solar flares with k NN on sparse autoencoder representations

The high dimensionality and strong correlations of magnetic field descriptors in the Space-Weather HMI Active Region Patches (SHARPs) data set present challenges for flare prediction studies. We perform a comparison of dimensionality-reduction pipelines that combine Canonical Factor Analysis (CFA) and sparse autoencoders (SAEs), coupled with a k-Nearest Neighbours classifier, to assess how different feature representations affect predictive skill. We find that both linear (CFA) and non-linear (SAE) compression enhance flare classification relative to using the raw parameters, with the non-linear SAE yielding the highest True Skill Statistic (TSS = 0.64) for binary flare/no-flare discrimination. Multiclass predictions remain limited by the imbalance of major flares and by overlapping feature distributions, but performance is consistent across multiple configurations. Our analysis also quantifies the redundancy of SHARP parameters, showing that only 5–7 compressed features are sufficient to capture the predictive information. These results provide a benchmark for dimensionality-reduction strategies in flare forecasting and demonstrate the value of sparse representations for handling correlated astrophysical parameter spaces.