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• W2978531943 abstract "Manifold learning can only be successful if enough data is available. If the data is too sparse, the geometrical and topological structure of the manifold extracted from the data cannot be recognised and the manifold collapses. In this paper we used data from a simulated two-dimensional double pendulum and tested how well several manifold learning methods could extract the expected manifold, a two-dimensional torus. The experiments were repeated while the data was downsampled in several ways to test the robustness of the different manifold learning methods. We also developed a neural network-based deep autoencoder for manifold learning and demonstrated that it performed in most of our test cases similarly or better than traditional methods such as principal component analysis and isomap." @default.
• W2978531943 created "2019-10-10" @default.
• W2978531943 creator A5049709657 @default.
• W2978531943 creator A5054614441 @default.
• W2978531943 date "2019-07-01" @default.
• W2978531943 modified "2023-09-22" @default.
• W2978531943 title "Testing the Robustness of Manifold Learning on Examples of Thinned-Out Data" @default.
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• W2978531943 doi "https://doi.org/10.1109/ijcnn.2019.8851939" @default.
• W2978531943 hasPublicationYear "2019" @default.
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