Multi-label output codes using canonical correlation analysis

Traditional error-correctingoutput codes (E-COCs) decompose a multi-class classification problem into many binary problems. Although it seems natural to use ECOCs for multi-label problems as well, doing so naively createsissues related to: the validity of the encoding, the efficiency of the decoding, the predictabilityofthegeneratedcodeword,and the exploitation of the label dependency. Using canonical correlation analysis, we propose an error-correcting code for multi-label classification. Labeldependencyischaracterized as the most predictable directions in the label space, which are extracted as canonical output variates and encoded into the codeword. Predictions for the codeword define a graphical model of…