［ポスター講演］Regularized multi-task learning for multi-dimensional log-density gradient estimation
○Ikko Yamane（Tokyo Inst. of Tech.）・Hiroaki Sasaki・Masashi Sugiyama（Univ. of Tokyo）
||(事前公開アブストラクト) Least-squares log-density gradient (LSLDG) is a recently proposed method to directly estimate the gradients of log-densities. In this work, we apply regularized multi-task learning to multi-dimensional LSLDG and show its usefulness.
||Log-density gradient estimation is a fundamental statistical problem and it has various practical applications such as clustering and a measure for non-Gaussianity. A naive two-step approach of rst estimating
the density and then taking its log-gradient does not perform well because an accurate density estimate does not
necessarily lead to an accurate log-density gradient estimate. To cope with this problem, a method to directly estimate the log-density gradient without density estimation was explored. However, even with the direct estimator,
high-dimensional log-density gradient estimation is still challenging. In this paper, we propose to apply regularized
multi-task learning to direct log-density gradient estimation and show its usefulness experimentally.
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||multi-task learning / log-density gradient estimation / / / / / /
||信学技報, vol. 114, no. 306, IBISML2014-58, pp. 177-183, 2014年11月.
||Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380