Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh
Institute of Information Science
In solving computer vision problems, adopting multiple descriptors to more precisely characterize the data has been a feasible
way for improving performance. The resulting data representations are typically high dimensional and assume diverse forms. Thus
finding a way to transform them into a unified space of lower dimension generally facilitates the underlying tasks, such as object
recognition or clustering. To this end, the proposed approach (termed as MKL-DR) generalizes the framework of multiple kernel
learning for dimensionality reduction, and introduces a new class of applications/techniques to address not only the supervised
learning problems but also the unsupervised and semisupervised ones.
IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (2011): 1147-1160.