Semi-Supervised Dimensionality Reduction based on Partial Least Squares for Visual Analysis of High Dimensional Data

dc.contributor.authorPaiva, Jose Gustavo S.en_US
dc.contributor.authorSchwartz, William Robsonen_US
dc.contributor.authorPedrini, Helioen_US
dc.contributor.authorMinghim, Rosaneen_US
dc.contributor.editorS. Bruckner, S. Miksch, and H. Pfisteren_US
dc.date.accessioned2015-02-28T07:03:12Z
dc.date.available2015-02-28T07:03:12Z
dc.date.issued2012en_US
dc.description.abstractDimensionality 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 data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume31
dc.identifier.doi10.1111/j.1467-8659.2012.03126.x
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2012.03126.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleSemi-Supervised Dimensionality Reduction based on Partial Least Squares for Visual Analysis of High Dimensional Dataen_US
Files