Multiple Kernel Learning for Dimensionality Reduction

—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. 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 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 pertainin...

• Type: Journal
• Year: 2011
• Where: PAMI
• Authors: Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
• Keywords: Dimensionality Reduction, Dimensionality Reduction Techniques, Multiple Kernel, Operations Research, PAMI 2011