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.
C. Fuh, Y. Lin and T. Liu, "Multiple Kernel Learning for Dimensionality Reduction," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 1147-1160, 2010.