BIG DATA SELF-LEARNING METHODOLOGY FOR THE ACCURATE QUANTIFICATION AND CLASSIFICATION OF SPECTRAL INFORMATION UNDER COMPLEX VARLABILITY AND MULTI-SCALE INTERFERENCE
INESC TEC - INSTITUTO DE ENGENHARIA DE SISTEMAS E COMPUTADORES, TECNOLOGIA E CIÊNCIA
发明人:
DA COSTA MARTINS, Rui Miguel
申请号：
WO2017IB56039
公开号：
WO2018060967(A1)
申请日:
2017.09.29
申请国别(地区):
世界知识产权组织国际局
年份:
2018
代理人:
摘要：
The present disclosure relates to a big data self-learning artificial intelligence methodology for the accurate quantification of metabolites classification of health conditions from spectral information, where complex biological variability and multi-scale spectral interference is present. In particular, this invention allows the breakdown of highly complex biological spectral signals into high dimensional feature space where local features of each sub-space are accurately correlated with both a specific metabolite concentration or a categorical condition. Such is achieved by a new self-learning method, that requires no human intervention. The developed artificial intelligence is able to establish its own knowledgebase when new data is fed by performing feature space transformations, searching directions of co-variance and optimizing local composition-spectral correlations. These methods allow the artificial intelligence to establish knowledge maps of both quantifications and classifications, that can be cas
