[PDF] Top 20 Nonlinear dimensionality reduction in climate data
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Nonlinear dimensionality reduction in climate data
... The dimensionality of the manifold (the optimum number of dimensions needed to capture the variability of the data) can be measured via the eigenvalues of the MDS pro- ... See full document
6
Extensions and Analysis of Local Non linear Techniques
... non-linear dimensionality reduction, such as Local Linear Embedding (LLE) and Laplacian eigenmap are not explicitly designed to preserve local features such as distances or ...local nonlinear ... See full document
6
Dimensionality reduction in nonparametric conditional density estimation with applications to nonlinear time series
... of data, and it is possible to allocate a suitable validation set, it may be most bene…cial to terminate the algorithm based on the out-of-sample perfor- mance on the validation ...of data or when a ... See full document
153
Characterization of Eukaryotic Core Promoters Based on Nonlinear Dimensionality Reduction
... the nonlinear dimensionality reduction technique, we obtain a low-dimensional representation of the CP ...principal dimensionality alone represents ...principal dimensionality instead ... See full document
5
Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space
... parameter data, and the second is an implementation phase in which a user can present (x, y) coordinates and an audio spectrum, resulting in a 13-dimensional vector of parameter state ...6 dimensionality ... See full document
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How to Solve Classification and Regression Problems on High-Dimensional Data with a Supervised Extension of Slow Feature Analysis
... Supervised learning from high-dimensional data, e.g., multimedia data, is a challenging task. We propose an extension of slow feature analysis (SFA) for supervised dimensionality reduction ... See full document
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Efficient Nonlinear Dimensionality Reduction for Pixel-wise Classification of Hyperspectral Imagery
... Superpixels are spatially connected sets of pixels with similar intensities/spectra that are con- structed and used to improve the computational efficiency and robustness of various image analysis systems. The mean ... See full document
150
A REVIEW ON DIMENSIONALITY REDUCTION USING COPULA APPROACH IN DATA MINING
... for data reduction based on PCA have been proposed (Sasikala & Balamuru- gan, ...sized data using nonlinear ...statistical dimensionality reduction methods applied to the ... See full document
15
Capturing Nonlinear Structure in Word Spaces through Dimensionality Reduction
... similarity data set of Rubenstein and Goodenough (1965), the word- relatedness data set of Finkelstein et ...antonymy data set of Deese (1964), which measures the degree to which high similarity cap- ... See full document
6
Feature subset selection and ranking for data dimensionality reduction
... the dimensionality reduction ...some nonlinear relationship, this assumption may become ...accommodate nonlinear relationships and to seek more powerful dependence measurement ... See full document
6
Dimensionality Reduction and Data Partitioning with Feature Hybridization Scheme
... 2) Nonlinear models, where more sophisticated features such as topic-related keywords are used as the input to build forecasting models using existing methods such as support vector machines or LASSO and 3) Time ... See full document
5
Global tests of P-values for multifactor dimensionality reduction models in selection of optimal number of target genes
... detect and characterize nonlinear complex gene-gene or gene-environment interactions (epistasis) that could be associated with disease susceptibility. The method was first proposed by Ritchie et al. [3] to detect ... See full document
17
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 ... See full document
40
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 ... See full document
25
Stochastic modelling of crack propagation in materials with random properties using isometric mapping for dimensionality reduction of nonlinear data sets
... high computational cost, particularly if Monte Carlo (MC) simulation approach is employed. Few frameworks have been proposed in the context of the continuum approaches and smeared crack models to study the stochastic ... See full document
45
High-Dimensionality Graph Data Reduction Based on Proposing A New Algorithm
... these data are presented in high-dimensional ...such data efficiently. In this case, dimensionality reduction is used to map a set of high- dimensional data into a low-dimensional space ... See full document
10
Factor regression for dimensionality reduction and data integration techniques with applications to cancer data
... a data visualisation tool: our aim is to investi- gate if MOM-SS has a competitive performance when the sample size significantly differs between batches and when the size of one of the samples is ... See full document
136
Study of Informative Value of Features in Rail Condition Monitoring
... For nonlinear regression, the assumption of Gaussianity for the measurement error combined with the maximum likelihood principle could be emphasized to promote this ...of nonlinear adaptive systems under ... See full document
10
1. Survey on the principal challenge of text mining
... input data is too large to be processed then the input data will be transformed into a reduced representation set of ...the dimensionality of data by transforming the original attribute space ... See full document
6
Novel Dimensionality Reduction Method for Symbolic Data using Coefficient of Variation
... In this paper, we propose a novel dimensionality reduction method of representing the set of features using smaller set of symbolic features. The intersection of intervals of pair samples is computed and ... See full document
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