Results 31 to 40 of about 375,641 (168)
Feature subset selection and ranking for data dimensionality reduction [PDF]
, 2007 A new unsupervised forward orthogonal search (FOS) algorithm is introduced for feature selection and ranking. In the new algorithm, features are selected in a stepwise way, one at a time, by estimating the capability of each specified candidate feature ...
Billings, S.A., Wei, H.L.
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Improving Dimensionality Reduction Projections for Data Visualization
Applied Sciences, 2023 In data science and visualization, dimensionality reduction techniques have been extensively employed for exploring large datasets. These techniques involve the transformation of high-dimensional data into reduced versions, typically in 2D, with the aim ...
Bardia Rafieian +2 more
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Visualizing dimensionality reduction of systems biology data
, 2012 One of the challenges in analyzing high-dimensional expression data is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a
A Hyvaerinen +31 more
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Nonlinear dimensionality reduction in climate data [PDF]
Nonlinear Processes in Geophysics, 2004 Linear methods of dimensionality reduction are useful tools for handling and interpreting high dimensional data. However, the cumulative variance explained by each of the subspaces in which the data space is decomposed may show a slow convergence that ...
A. J. Gámez +3 more
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Linguistic Geometries for Unsupervised Dimensionality Reduction [PDF]
, 2010 Text documents are complex high dimensional objects. To effectively visualize such data it is important to reduce its dimensionality and visualize the low dimensional embedding as a 2-D or 3-D scatter plot.
Balasubramanian, Krishnakumar +2 more
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Arabic L2 readability assessment: Dimensionality reduction study
Journal of King Saud University: Computer and Information Sciences, 2022 Readability is a measure that associates a written text to a reader’s skill or grade level. Readability assessment is very important in the field of second or foreign language (L2) learning.
Naoual Nassiri +2 more
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Sequential Dimensionality Reduction for Extracting Localized Features
, 2016 Linear dimensionality reduction techniques are powerful tools for image analysis as they allow the identification of important features in a data set. In particular, nonnegative matrix factorization (NMF) has become very popular as it is able to extract ...
Casalino, Gabriella, Gillis, Nicolas
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Dimensionality reduction in data from LASER applications [PDF]
مجلة جامعة الانبار للعلوم الصرفة, 2012 Redundant variables not only in LASER applications, but in all experimental works are disturbing statistical analysis as a result of highly correlation among them.
Imad H.Aboud, Qassim M. Jameel
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Dimensionality Reduction by Similarity Distance-Based Hypergraph Embedding
Atmosphere, 2022 Dimensionality reduction (DR) is an essential pre-processing step for hyperspectral image processing and analysis. However, the complex relationship among several sample clusters, which reveals more intrinsic information about samples but cannot be ...
Xingchen Shen, Shixu Fang, Wenwen Qiang
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BIP: A dimensionality reduction for image indexing
ICT Express, 2019 Searching on internet is one of the daily task done by millions of users around the Globe. There is an urge for effective indexing scheme for unstructured data, which provide better search results. The image, content report, and site pages are said to be
Minu R.I. +3 more
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