公开(公告)号：US20200311544A1

公开(公告)日：2020-10-01

申请号：US16651637

申请日：2017-09-28

Applicant: Agency for Science, Technology and Research

Inventor： Savitha Ramasamy ,  Rajaraman Kanagasabai

IPC: G06N3/08 ,  G06N3/04

Abstract: A method, a computer-readable medium, and an apparatus for feature learning are provided. The apparatus may receive a data sample as an input to a feature learning model. The apparatus may calculate a reconstruction error based on the data sample and a plurality of features of the feature learning model. The apparatus may determine whether the reconstruction error satisfies a first threshold. The apparatus may add a feature into the feature learning model to represent the data sample if the data sample satisfies the first threshold. The apparatus may determine whether the reconstruction error satisfies a second threshold. The apparatus may ignore the data sample if the reconstruction error satisfies the second threshold. The apparatus may update the weights associated with the plurality of features of the feature learning model if the reconstruction error satisfies neither the first threshold nor the second threshold.

公开(公告)号：US11657270B2

公开(公告)日：2023-05-23

申请号：US16651637

申请日：2017-09-28

Applicant: Agency for Science, Technology and Research

Inventor： Savitha Ramasamy ,  Rajaraman Kanagasabai

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/04

Abstract: A method, a computer-readable medium, and an apparatus for feature learning are provided. The apparatus may receive a data sample as an input to a feature learning model. The apparatus may calculate a reconstruction error based on the data sample and a plurality of features of the feature learning model. The apparatus may determine whether the reconstruction error satisfies a first threshold. The apparatus may add a feature into the feature learning model to represent the data sample if the data sample satisfies the first threshold. The apparatus may determine whether the reconstruction error satisfies a second threshold. The apparatus may ignore the data sample if the reconstruction error satisfies the second threshold. The apparatus may update the weights associated with the plurality of features of the feature learning model if the reconstruction error satisfies neither the first threshold nor the second threshold.