HhP:Hierarchical Heterogeneity Analysis via Penalization
In medical research, supervised heterogeneity analysis has important implications. Assume that there are two types of
features. Using both types of features, our goal is to conduct
the first supervised heterogeneity analysis that satisfies a
hierarchical structure. That is, the first type of features
defines a rough structure, and the second type defines a nested
and more refined structure. A penalization approach is
developed, which has been motivated by but differs
significantly from penalized fusion and sparse group
penalization. Reference: Ren, M., Zhang, Q., Zhang, S., Zhong,
T., Huang, J. & Ma, S. (2022). "Hierarchical cancer
heterogeneity analysis based on histopathological imaging
features". Biometrics, <doi:10.1111/biom.13426>.
