Issue No. 06 - June (2011 vol. 33)
Yen-Yu Lin , Academia Sinica, Taipei
Tyng-Luh Liu , Academia Sinica, Taipei
Chiou-Shann Fuh , National Taiwan University, Taipei
In solving complex visual learning tasks, 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. Hence, finding a way of transforming 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 MKL-DR) generalizes the framework of multiple kernel learning for dimensionality reduction, and distinguishes itself with the following three main contributions: First, our method provides the convenience of using diverse image descriptors to describe useful characteristics of various aspects about the underlying data. Second, it extends a broad set of existing dimensionality reduction techniques to consider multiple kernel learning, and consequently improves their effectiveness. Third, by focusing on the techniques pertaining to dimensionality reduction, the formulation introduces a new class of applications with the multiple kernel learning framework to address not only the supervised learning problems but also the unsupervised and semi-supervised ones.
Dimensionality reduction, multiple kernel learning, object categorization, image clustering, face recognition.
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh, "Multiple Kernel Learning for Dimensionality Reduction", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 1147-1160, June 2011, doi:10.1109/TPAMI.2010.183