| Budget Amount *help |
¥3,860,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2009: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2008: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2007: ¥1,000,000 (Direct Cost: ¥1,000,000)
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| Research Abstract |
The set of fixed number of orthogonal subspaces each of which has fixed dimensionality can be regarded as a curved space, termed the flag manifold. Using Riemannian geometry we proposed new optimization methods on this class of manifolds such as Riemannian gradient descent, conjugate gradient method and hybrid MCMC geodesic method. Based on these Riemannian optimization methods new learning algorithms for independent subspace analysis were proposed and their effectiveness over conventional algorithms was experimentally confirmed.
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