Nonlinear dimensionality reduction (manifold learning)
Nonlinear Dimensionality Reduction for Visualization
... making. Dimensionality reduction (or manifold learning) tools may be used for visualization if the resulting dimension is constrained to be 2 or ...numerous nonlinear dimensionality ...
Learning a kernel matrix for nonlinear dimensionality reduction
... for nonlinear dimensionality reduction Abstract We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional ...a nonlinear feature space, we ...
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Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
... Nonlinear dimensionality reduction methods are often used to visualize high-dimensional data, al- though the existing methods have been designed for other related tasks such as manifold ...
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Nonlinear dimensionality reduction methods for synthetic biology biobricks visualization
... biobricks, therefore current distance matrix could not be applied on biobricks. Nonlinear dimensionality reduction is mainly based on manifold learning and could handle data’s nonlinear prop- ...
Nonlinear dimensionality reduction in climate data
... Conclusions Nonlinear dimensionality reduction methods provide a use- ful way of analysing and modeling high dimensional data when nonlinear interactions are ...the reduction of the ...
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Nonlinear dimensionality reduction in climate data
... Conclusions Nonlinear dimensionality reduction methods provide a use- ful way of analysing and modeling high dimensional data when nonlinear interactions are ...the reduction of the ...
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Compressed Spectral Regression for Efficient Nonlinear Dimensionality Reduction
... efficient nonlinear dimensionality reduction algorithm termed Com- pressed Spectral Regression (CSR) in this ...the nonlinear projective function can be ef- ficiently ...
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Using Nonlinear Dimensionality Reduction in 3D Figure Animation
... for nonlinear dimensionality reduction, Isomap and ...the reduction to be more visually compelling in free-form motions such as dancing, whereas in cyclic locomotion such as walking they are ...
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Characterization of Eukaryotic Core Promoters Based on Nonlinear Dimensionality Reduction
... a nonlinear dimensionality reduction algorithm – Isomap on the averaged promoter model, which is described by a set of physicochemical parameters, to separate a comprehensive structural ...
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Large-scale nonlinear dimensionality reduction for network intrusion detection
... 5- Department of Botany, University of Kashmir Abstract . Network intrusion detection (NID) is a complex classification problem. In this paper, we combine classification with recent and scalable nonlinear ...
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Parametric nonlinear dimensionality reduction using kernel t-SNE
... Figure 1: Evaluation of various nonlinear dimensionality reduction ap- proaches together with our proposed kernel mapping on the usps data set. time on the right coordinate axis. The quality of the ...
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Isomap nonlinear dimensionality reduction and bimodality of Asian monsoon convection
... 4. Summary and Discussion [ 19 ] We have investigated monsoon intraseasonal vari- ability using nonlinear dimensionality reduction based on Isomap of ERA-40 daily SLP anomalies over the ASM region ...
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ENSO dynamics in current climate models: an investigation using nonlinear dimensionality reduction
... There are four further issues with the Isomap algorithm that deserve comment, and that can provide a basis for com- parison between Isomap and the NLPCA method (Hsieh, 2004). First is the question of the sensitivity of ...
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Learning and Recognition of Object Manipulation Actions Using Linear and Nonlinear Dimensionality Reduction
... and dimensionality reduction for cases where actions are very similar to each other in terms of arm ...a nonlinear dimensionality reduction techniques: Principal Component Analysis and ...
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The K-LLE Algorithm for Nonlinear Dimensionality Reduction of Large-Scale Hyperspectral Data
... addresses nonlinear dimensionality reduction by means of locally linear embedding (LLE) for large-scale hyperspectral ...to dimensionality reduction of large-scale hyperspectral data ...
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Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction
... Dimensionality reduction (DR) is a widely used technique to address the curse of dimensionality when high- dimensional remotely sensed data, such as multi-temporal or hyperspectral imagery, are ...
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Nonlinear Dimensionality Reduction by Manifold Unfolding
... of dimensionality’ continues to severely challenge machine learning and data mining ...the dimensionality of data increases, which quickly expands the volume of the input space, such that the available data ...
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Efficient Nonlinear Dimensionality Reduction for Pixel-wise Classification of Hyperspectral Imagery
... used for denoising and the other is used to estimate the endmembers. To sum up, different kinds of autoencoders have been widely used in a variety of HSI applications, including classification, unmixing, target ...
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Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering
... of nonlinear data analysis ...with nonlinear effects in metabolite line shape in tumour tissue. Nonlinear effects also occur due to distortions in relative metabolite peak heights for peripheral MRSI ...
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Exploring the dimensionality of speech using manifold learning and dimensionality reduction methods
... The aim of the work presented in this dissertation is to evaluate the performance of a number of linear and nonlinear dimensionality reduction methods when applied to speech data and to [r] ...
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