A new paradigm for high‐dimensional data: Distance‐based semiparametric feature aggregation framework via between‐subject attributes
Jinyuan Liu,
Xinlian Zhang,
Tuo Lin
et al.
Abstract:This article proposes a distance‐based framework incentivized by the paradigm shift toward feature aggregation for high‐dimensional data, which does not rely on the sparse‐feature assumption or the permutation‐based inference. Focusing on distance‐based outcomes that preserve information without truncating any features, a class of semiparametric regression has been developed, which encapsulates multiple sources of high‐dimensional variables using pairwise outcomes of between‐subject attributes. Further, we pro… Show more
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