Feature selection versus feature compression in the building of calibration models from FTIR-spectrophotometry datasets

Talanta

Volumn 88, Issue , 2012, Pages 95-103

  Vergara, Alexander ^a   Llobet, Eduard ^b  

^a UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^b UNIVERSITAT ROVIRA I VIRGILI   (Spain)

Author keywords

Feature compression; Feature selection; FTIR spectrophotometry; Minimum Redundancy Maximum Relevance; Regression models; Self organizing map

Indexed keywords

BINARY MIXTURES; CHEMICAL ANALYSIS; CHEMICAL INDUSTRY; CONFORMAL MAPPING; FOURIER TRANSFORM INFRARED SPECTROSCOPY; REDUNDANCY; REGRESSION ANALYSIS; SELF ORGANIZING MAPS; SPECTROPHOTOMETRY;

DIMENSIONALITY REDUCTION METHOD; FEATURE COMPRESSION; FTIR SPECTROPHOTOMETRIES; HIGH-DIMENSIONAL FEATURE SPACE; MINIMUM REDUNDANCY-MAXIMUM RELEVANCES; PATTERN RECOGNITION METHOD; REGRESSION MODEL; STATISTICAL CHARACTERISTICS;

FEATURE EXTRACTION;

EID: 83755188762     PISSN: 00399140     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.talanta.2011.10.014     Document Type: Article

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