Patterns classification of nonlinear multi-dimensional time series ...
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Patterns classification of nonlinear multi-dimensional time series based on manifold learning. Abstract: The multi-sensor signals in industrial process is ...
be designed by using support vector machines (SVM) and k nearest neighbor algorithm (knn) in the low dimensional space. The proposed method can greatly ...
The proposed method can greatly preserve the consistency of data local neighborhood structure, effectively extract the low dimensional manifold features ...
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In this paper, we present a novel method that learns nonlinear mapping from time series data to their intrinsic coordinates on the u nderlying manifold. Our ...
Dec 14, 2023 · The core idea of manifold learning is based on the manifold assumption, which posits that data are distributed on a smooth low-dimensional ...
Manifold learning is an approach to non-linear dimensionality reduction. Algorithms for this task are based on the idea that the dimensionality of many data ...
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We address the problem of simultaneous nonlinear dimensionality reduction and clustering of data points drawn from multiple linear and nonlinear manifolds. We ...
In this paper, we propose a novel nonlinear manifold classification algorithm based on a well-known manifold leaning method called Locally Linear Embedding (LLE) ...