Discriminative Fast Soft Competitive Learning [chapter]

Frank-Michael Schleif
2014 Lecture Notes in Computer Science  
Proximity matrices like kernels or dissimilarity matrices provide nonstandard data representations common in the life science domain. Here we extend fast soft competitive learning to a discriminative and vector labeled learning algorithm for proximity data. It provides a more stable and consistent integration of label information in the cost function solely based on a give proximity matrix without the need of an explicite vector space. The algorithm has linear computational and memory
more » ... ts and performs favorable to traditional techniques.
doi:10.1007/978-3-319-11179-7_11 fatcat:ejlp7suhwfa7hkxqgikgsmwtua