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Two-Phase Mapping for Projecting Massive Data Sets
(IEEE COMPUTER SOC, 2010)
Most multidimensional projection techniques rely on distance (dissimilarity) information between data instances to embed high-dimensional data into a visual space. When data are endowed with Cartesian coordinates, an extra ...
A spectral envelope approach towards effective SVM-RFE on infrared data
(Elsevier Science, 2016-02)
Infrared spectroscopy data is characterized by the presence of a huge number of variables. Applications of infrared spectroscopy in the mid-infrared (MIR) and near-infrared (NIR) bands are of widespread use in many fields. ...
A projection pursuit framework for supervised dimension reduction of high dimensional small sample datasets
(ElsevierAmsterdam, 2015-02)
The analysis and interpretation of datasets with large number of features and few examples has remained as a challenging problem in the scientific community, owing to the difficulties associated with the curse-of-the-dim ...
A multivariate geostatistical approach for landscape classification from remotely sensed image data
(2015)
This paper proposes a methodology to address the classification of images that have been acquired from remote sensors. One common problem in classification is the high dimensionality of multivariate characteristics. The ...
Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach
(2019-06)
Based on a General Dynamic Factor Model with infinite-dimensional factor space, we develop a new estimation and forecasting procedures for conditional covariance matrices in high-dimensional time series. The performance ...
Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data
(WILEY-BLACKWELLHOBOKEN, 2012)
Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve ...
Estimating sufficient reductions of the predictors in abundant high-dimensional regressions
(Institute of Mathematical Statistics, 2012-02)
We study the asymptotic behavior of a class of methods for sufficient dimension reduction in high-dimension regressions, as the sample size and number of predictors grow in various alignments. It is demonstrated that these ...
Data classification: Dimensionality reduction using combined and non-combined multidimensional projection techniques
(2018-12-13)
Dimensionality Reduction is a commonly used method to reduce the number of dimensions of data. In this work, we verified its influence in classification process using combinations of projection techniques as dimensionality ...
Two-dimensional synthesis of silver nanoparticle in situ Langmuir films from the reduction of silver sulfadiazine
(2022-03-31)
Two–dimensional (2D) arrangement of monodisperse metallic nanoparticles (NPs) has attracted attention because of the unique properties of this assembly. These properties offer new perspectives for several applications. ...