Model Averaging for Manifold Learning
Abstract: Manifold learning aims to extract information of high-dimensional data and provide low-dimensional representations while preserving nonlinear structures of the input data. Numerous manifold learning algorithms have been proposed in the literature. We develop a model averaging procedure to combine different manifold learning algorithms for enhancing the robustness of the result. Toward this goal, we propose a new quality metric that is tuning-free and scale-invariant by utilizing the Mahalanobis distance. The quality metric can also be used for selection of tuning parameters. Through synthetic and real data examples, we show that the new metric outperforms existing ones and
the model averaging outcome provides a unanimous outcome that is always competitive with respect to visualization or classification under different contexts.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: There are minor revisions in response to Reviewer o362, including
1. The citation of Zhang et al. (2013) in Introduction.
2. The specification that, in Section 5.2, we apply the manifold learning algorithms for all the data first and then divide them into training and testing datasets.
Assigned Action Editor: ~Bamdev_Mishra1
Submission Number: 983
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