Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Frequently Asked Questions
Frequently Asked Questions
Communities & Collections
Communities & Collections
Nonlinear supervised dimensionality reduction via smooth regular embeddings
Download
index.pdf
Date
2018
Author
Örnek, Cem
Metadata
Show full item record
Item Usage Stats
5
views
4
downloads
The recovery of the intrinsic geometric structures of data collections is an important problem in data analysis. Supervised extensions of several manifold learning approaches have been proposed in the recent years. Meanwhile, existing methods primarily focus on the embedding of the training data, and the generalization of the embedding to initially unseen test data is rather ignored. In this work, we build on recent theoretical results on the generalization performance of supervised manifold learning algorithms. Motivated by these performance bounds, we propose a supervised manifold learning method that computes a nonlinear embedding while constructing a smooth and regular interpolation function that extends the embedding to the whole data space in order to achieve satisfactory generalization. The embedding and the interpolator are jointly learnt such that the Lipschitz regularity of the interpolator is imposed while ensuring the separation between different classes. Experimental results on several image data sets show that the proposed method yields quite satisfactory performance in comparison with other supervised dimensionality reduction algorithms and traditional classifiers.
Subject Keywords
Intelligent transportation systems.
,
Electronics in transportation.
URI
http://etd.lib.metu.edu.tr/upload/12621917/index.pdf
https://hdl.handle.net/11511/27197
Collections
Graduate School of Natural and Applied Sciences, Thesis