# Fast Approximation of Rotations and Hessians matrices

arXiv: Learning, 2014.

Cited by: 2|Bibtex|Views120
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Abstract:

A new method to represent and approximate rotation matrices is introduced. The method represents approximations of a rotation matrix $Q$ with linearithmic complexity, i.e. with $\frac{1}{2}n\lg(n)$ rotations over pairs of coordinates, arranged in an FFT-like fashion. The approximation is "learned" using gradient descent. It allows to re...More

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