BIG DATA SELF-LEARNING METHODOLOGY FOR THE ACCURATE QUANTIFICATION AND CLASSIFICATION OF SPECTRAL INFORMATION UNDER COMPLEX VARLABILITY AND MULTI-SCALE INTERFERENCE
专利汇可以提供BIG DATA SELF-LEARNING METHODOLOGY FOR THE ACCURATE QUANTIFICATION AND CLASSIFICATION OF SPECTRAL INFORMATION UNDER COMPLEX VARLABILITY AND MULTI-SCALE INTERFERENCE专利检索，专利查询，专利分析的服务。并且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 cashed for higher computational performance. In particular, direct search comprises of finding across the feature space data and dimensions that allow a direct linear correspondence between metabolic composition and spectral bands variance. Moreover, a similar approach is derived for defining the convex hull regions of different class of health conditions from body fluid spectra. Such results in the creation of knowledge maps for both quantification and classification. The present invention also allows to evaluate 'a priori' the predictability, accuracy and precision of new estimates. Furthermore, this invention provides a self-learning approach to de definition of the global feature space using big data, for its correct characterization under high variability, accurate detection of local anomalies, as well as, outliers that can contaminate the knowledge base. This invention is applicable to all regions of the electro-magnetic spectra used in spectroscopy analysis (x-ray, uv, vis, nir, ir, far-ir and microwaves), or with any other type of spectroscopy (absorvance, reflectance, fluorescence, phosphorescence, Raman scattering) where complex multi-scale interference and biological variability is present. It further extends to fields of non-destructive, non-invasive spectroscopy applications in fields such as healthcare, veterinary, biotechnology, pharmaceutical, food and agriculture.，下面是BIG DATA SELF-LEARNING METHODOLOGY FOR THE ACCURATE QUANTIFICATION AND CLASSIFICATION OF SPECTRAL INFORMATION UNDER COMPLEX VARLABILITY AND MULTI-SCALE INTERFERENCE专利的具体信息内容。
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