  Sunspots, as seen in white light or continuum images, are associated with
regions of high magnetic activity on the Sun, visible on magnetogram images.
Their complexity is correlated with explosive solar activity and so classifying
these active regions is useful for predicting future solar activity. Current
classification of sunspot groups is visually based and suffers from bias.
Supervised learning methods can reduce human bias but fail to optimally
capitalize on the information present in sunspot images. This paper uses two
image modalities (continuum and magnetogram) to characterize the spatial and
modal interactions of sunspot and magnetic active region images and presents a
new approach to cluster the images. Specifically, in the framework of image
patch analysis, we estimate the number of intrinsic parameters required to
describe the spatial and modal dependencies, the correlation between the two
modalities and the corresponding spatial patterns, and examine the phenomena at
different scales within the images. To do this, we use linear and nonlinear
intrinsic dimension estimators, canonical correlation analysis, and
multiresolution analysis of intrinsic dimension.
