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• W2806380130 abstract "We present a novel view of nonlinear manifold learning using derivative-free optimization techniques. Specifically, we propose an extension of the classical multi-dimensional scaling (MDS) method, where instead of performing gradient descent, we sample and evaluate possible moves in a sphere of fixed radius for each point in the embedded space. A fixed-point convergence guarantee can be shown by formulating the proposed algorithm as an instance of General Pattern Search (GPS) framework. Evaluation on both clean and noisy synthetic datasets shows that pattern search MDS can accurately infer the intrinsic geometry of manifolds embedded in high-dimensional spaces. Additionally, experiments on real data, even under noisy conditions, demonstrate that the proposed pattern search MDS yields state-of-the-art results." @default.
• W2806380130 created "2018-06-13" @default.
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• W2806380130 date "2018-06-01" @default.
• W2806380130 modified "2023-09-27" @default.
• W2806380130 title "Pattern Search Multidimensional Scaling" @default.
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• W2806380130 hasPublicationYear "2018" @default.
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