Nonlinear dimensionality reduction from automatic supervision
Nonlinear Dimensionality Reduction by Manifold Unfolding
... most dimensionality reduction methods supposedly pre- serve the local patterns ...in dimensionality reduction is to create a neighborhood graph for the sampled points, and define the local ...
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Nonlinear dimensionality reduction in climate data
... be nonlinear, may lead to erronous conclusions when subtracting a constant annual cycle from SST data under consideration, as usually done in the analysis of ENSO ...
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Nonlinear dimensionality reduction in climate data
... be nonlinear, may lead to erronous conclusions when subtracting a constant annual cycle from SST data under consideration, as usually done in the analysis of ENSO ...
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Learning a kernel matrix for nonlinear dimensionality reduction
... Isomap from finite data sets can have negative ...it from a large multiple of the identity ...intrinsic dimensionality of underlying manifolds (Saul & Roweis, ...rived from the discrete ...
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Compressed Spectral Regression for Efficient Nonlinear Dimensionality Reduction
... Table 1, 2 and 3 show the clustering performance on train- ing set, the subspace learning time and the performance on test set, respectively. The evaluations were conducted with the cluster numbers ranging from ...
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Using Nonlinear Dimensionality Reduction in 3D Figure Animation
... 3.3 Dimensionality Reduction An important question in re-sequencing motion capture data is how to handle global degrees of ...bedding from a start frame to an end frame, it is necessary to have one ...
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Characterization of Eukaryotic Core Promoters Based on Nonlinear Dimensionality Reduction
... The scanning is done by the online server GPMiner [20]. We then select fifty CPs (N=50) from the two groups respectively and calculate the for both CP collections. The result shows that the same feature appears in ...
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Large-scale nonlinear dimensionality reduction for network intrusion detection
... HAL Id: hal-01517215 https://hal.archives-ouvertes.fr/hal-01517215 Submitted on 27 Aug 2019 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether ...
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Isomap nonlinear dimensionality reduction and bimodality of Asian monsoon convection
... the nonlinear struc- ture of the monsoon phases, the active phase being replaced by a dipolar phase which has the structure of active con- ditions over east China and the East China Sea, and with break conditions ...
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Capturing Nonlinear Structure in Word Spaces through Dimensionality Reduction
... results from both experiments indicate that LPP is capable of accurately representing distri- butional information in a much lower dimensional ...of nonlinear dimensionality ...
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Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
... different from manifold learning, in case the inherent dimensionality of the manifold is higher than two and the manifold cannot be represented perfectly in two ...on dimensionality reduction ...
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Efficient Nonlinear Dimensionality Reduction for Pixel-wise Classification of Hyperspectral Imagery
... features from different kinds of data, and it is straightforward to provide them with supervised ...other nonlinear DR methods, we develop our semi-supervised deep autoencoder network (SSDAN) in the next ...
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Dimensionality Reduction and Visualization
... from Venna, Peltonen, Nybo, Aidos, and Kaski, “Information retrieval perspective to nonlinear dimensionality reduction for data visualization”,Journal of Machine Learning Research, 2010[r] ...
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ENSO dynamics in current climate models: an investigation using nonlinear dimensionality reduction
... Independent of the exact mechanism of variability, the spatial coherence of ENSO in the Pacific leads us to ex- pect that there should be a low-dimensional model that cap- tures at least some of the variability in the ...
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Learning and Recognition of Object Manipulation Actions Using Linear and Nonlinear Dimensionality Reduction
... it from all the samples, estimate the covariance matrix and estimate its SVD, ...the dimensionality reduction was following: single sensor (from 9 to 3), two sensors (18 to 5), three sensors ...
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Dimensionality reduction in nonparametric conditional density estimation with applications to nonlinear time series
... conclusion from Table ...the nonlinear models, and in particular for the nonlinear ARCH model with relatively small sample sizes (n = 100 or ...
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Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering
... path from tissue metabolite concentration to MRS signal intensity and that spectra from mixed tissue types will add in linear proportion, our results suggest benefits for application of nonlinear ...
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The K-LLE Algorithm for Nonlinear Dimensionality Reduction of Large-Scale Hyperspectral Data
... This work aims at a general method to avoid calculating large-scale spectral decomposition, and in the meantime, embedding this idea into the LLE algorithm, enabling the algorithm to learn the underlying manifold ...
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Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images
... derived from the dimensionality reduction, we propose the use of the NLPCA to project the original data into a reduced dimension- ality subspace (or feature space) by extracting mean- ingful ...
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An Actual Survey of Dimensionality Reduction
... (MDS) from a modern and more exact consideration view of point, specially a defined objective stress function arises in this ...of nonlinear dimensionality reduction by computing ...
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