Embedding Learning.

ABSTRACT: Numerical embedding has become one standard technique for processing and analyzing unstructured data that cannot be expressed in a predefined fashion. It stores the main characteristics of data by mapping it onto a numerical vector. An embedding is often unsupervised and constructed by transfer learning from large-scale unannotated data. Given an embedding, a downstream learning method, referred to as a two-stage method, is applicable to unstructured data. In this article, we introduce a novel framework of embedding learning to deliver a higher learning accuracy than the two-stage method while identifying an optimal learning-adaptive embedding. In particular, we propose a concept of U-minimal sufficient learning-adaptive embeddings, based on which we seek an optimal one to maximize

SUBMITTER: Dai B 

PROVIDER: S-EPMC10022456 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature