Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation
By Ikko Yamane, Hiroaki Sasaki, Masashi Sugiyama. 2014
8 citations recorded by the source corpus.
Log-density gradient estimation is a fundamental statistical problem and possesses various practical applications such as clustering and measuring nongaussianity. A naive two-step approach of first estimating the density and then taking its log gradient is unreliable 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 has been explored and demonstrated to work much better than the two-step method. The objective of this letter is to improve the performance of this direct method in multidimensional cases. Our idea is to regard the problem of log-density gradient estimation in each dimension as a task and apply regularized multitask learning to the direct log-density gradient estimator. We experimentally demonstrate the usefulness of the proposed multitask method in log-density gradient estimation and mode-seeking clustering.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Qiang Yang
- Yu Zhang
- Mingsheng Long
- Jianmin Wang
- Dongrui Wu
- Sinno Jialin Pan
- Sebastian Ruder
- Philip S. Yu
Citations in the corpus
- A Survey on Multi-Task Learning (2017, methodology): “A similar formulation to [71] is proposed in [73] to estimate themean of multiple distributions by learning pairwise task relations and another similar formulation is proposed in [74] for log-density gradient estimation.”