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Talk
Joint Dimensionality Reduction and Metric Learning: A Geometric Take
Mehrtash Harandi · Mathieu Salzmann · Richard I Hartley

Mon Aug 07 11:06 PM -- 11:24 PM (PDT) @ C4.6 & C4.7

To be tractable and robust to data noise, existing metric learning algorithms commonly rely on PCA as a pre-processing step. How can we know, however, that PCA, or any other specific dimensionality reduction technique, is the method of choice for the problem at hand? The answer is simple: We cannot! To address this issue, in this paper, we develop a Riemannian framework to jointly learn a mapping performing dimensionality reduction and a metric in the induced space. Our experiments evidence that, while we directly work on high-dimensional features, our approach yields competitive runtimes with and higher accuracy than state-of-the-art metric learning algorithms.

Author Information

Mehrtash Harandi (Data61)
Mathieu Salzmann (EPFL)
Richard I Hartley (Australian National University)

Richard Hartley is a member of the computer vision group in the Research School of Engineering, at the Australian National University, where he has been since January, 2001. He is a joint leader of the Computer Vision group in NICTA, a government funded research laboratory. Dr. Hartley worked at the General Electric Research and Development Center from 1985 to 2001, working first in VLSI design, and later in computer vision. He became involved with Image Understanding and Scene Reconstruction working with GE's Simulation and Control Systems Division. In 1991, he began an extended research effort in the area of applying projective geometry techniques to reconstruction using calibrated and semi-calibrated cameras. This research direction was one of the dominant themes in computer vision research throughout the 1990s. In 2000, he co-authored (with Andrew Zisserman) a book on Multiview Geometry in Computer Vision, summarizing the previous decade’s research in this area.

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