Log-ratio lasso: Scalable, sparse estimation for log-ratio models.

ABSTRACT: Positive-valued signal data is common in the biological and medical sciences, due to the prevalence of mass spectrometry other imaging techniques. With such data, only the relative intensities of the raw measurements are meaningful. It is desirable to consider models consisting of the log-ratios of all pairs of the raw features, since log-ratios are the simplest meaningful derived features. In this case, however, the dimensionality of the predictor space becomes large, and computationally efficient estimation procedures are required. In this work, we introduce an embedding of the log-ratio parameter space into a space of much lower dimension and use this representation to develop an efficient penalized fitting procedure. This procedure serves as the foundation for a two-step fitting proced

SUBMITTER: Bates S 

PROVIDER: S-EPMC9470385 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature