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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.<\/jats:p>","DOI":"10.1162\/neco_a_00844","type":"journal-article","created":{"date-parts":[[2016,5,12]],"date-time":"2016-05-12T21:16:46Z","timestamp":1463087806000},"page":"1388-1410","source":"Crossref","is-referenced-by-count":3,"title":["Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation"],"prefix":"10.1162","volume":"28","author":[{"given":"Ikko","family":"Yamane","sequence":"first","affiliation":[{"name":"Graduate School of Frontier Sciences, University of Tokyo, Kashiwa-shi, Chiba 277-8561, Japan"}]},{"given":"Hiroaki","family":"Sasaki","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma-shi, Nara 630-0192, Japan"}]},{"given":"Masashi","family":"Sugiyama","sequence":"additional","affiliation":[{"name":"Graduate School of Frontier Sciences, University of Tokyo, Kashiwa-shi, Chiba 277-8561, Japan"}]}],"member":"281","reference":[{"key":"B1","first-page":"1817","volume":"6","author":"Ando R. 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