Abstract
Regression is aimed at estimating the conditional expectation of output given input, which is suitable for analyzing functional relation between input and output. On the other hand, when the conditional density with multiple modes is analyzed, modal regression comes in handy. Partial mean shift (PMS) is a promising method of modal regression, which updates data points toward conditional modes by gradient ascent. In the implementation, PMS first obtains an estimate of the joint density by kernel density estimation and then computes its derivative for gradient ascent. However, this two-step approach can be unreliable because a good density estimator does not necessarily mean a good density derivative estimator. In this paper, we propose a novel method for modal regression based on direct estimation of the log-density derivative without density estimation. Experiments show the superiority of our direct method over PMS.
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Notes
- 1.
The intervals of \(\sigma _{\mathrm {x}}\) and \(h_{\mathrm {x}}\) (or \(\sigma _{\mathrm {y}}\) and \(h_{\mathrm {y}}\)) were further changed by multiplying the median value of \(|x_i^{(j)}-x_k^{(j)}|\) with respect to i, j, k (or \(|y_i-y_k|\) with respect to i, k).
- 2.
The datasets were downloaded at http://www.blackwellpublishing.com/rss.
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Acknowledgments
HS was supported by KAKENHI 15H06103 and MS was supported by KAKENHI 25700022.
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Sasaki, H., Ono, Y., Sugiyama, M. (2016). Modal Regression via Direct Log-Density Derivative Estimation. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_13
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DOI: https://doi.org/10.1007/978-3-319-46672-9_13
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