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Resistant estimates for high dimensional and functional data based on random projections
(Elsevier, 2012-09)
We herein propose a new robust estimation method based on random projections that is adaptive and automatically produces a robust estimate, while enabling easy computations for high or infinite dimensional data. Under some ...
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 ...
LoCH: a neighborhood-based multidimensional projection technique for high-dimensional sparse spaces
(ElsevierAmsterdam, 2015-02)
On the last few years multidimensional projection techniques have advanced towards defining faster and user-centered approaches. However,most of existing methods are designed as generic tools without considering particular ...
iStar (i*): An interactive star coordinates approach for high-dimensional data exploration
(Elsevier Ltd, 2016)
Star Coordinates is an important visualization method able to reveal patterns and groups from multidimensional data while still showing the impact of data attributes in the formation of such patterns and groups. Despite ...
Dimensionality reduction for visualization of normal and pathological speech data
(Elsevier, 2009-07)
For an adequate analysis of pathological speech signals, a sizeable number of parameters is required, such as those related to jitter, shimmer and noise content. Often this kind of high-dimensional signal representation ...
Semi-Supervised Dimensionality Reduction based on Partial Least Squares for Visual Analysis of High Dimensional Data
(Wiley-BlackwellHoboken, 2012)
Informational content of cosine and other similarities calculated from high-dimensional Conceptual Property Norm data.
To study concepts that are coded in language, researchers often collect lists of conceptual properties produced by human subjects. From these data, diferent measures can be computed. In particular, inter-concept similarity ...
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 ...
Cross-entropy embedding of high-dimensional data using the neural gas model
(PERGAMON-ELSEVIER SCIENCE LTD, 2005-06)
A cross-entropy approach to mapping high-dimensional data into a low-dimensional space embedding is presented. The method allows to project simultaneously the input data and the codebook vectors, obtained with the Neural ...
HiPP: A Novel Hierarchical Point Placement Strategy and its Application to the Exploration of Document Collections
(IEEE COMPUTER SOC, 2008)
Point placement strategies aim at mapping data points represented in higher dimensions to bi-dimensional spaces and are frequently used to visualize relationships amongst data instances. They have been valuable tools for ...