Least Square Projection: A Fast High-Precision Multidimensional Projection Technique and Its Application to Document Mapping

By Fernando Vieira Paulovich, Luis Gustavo Nonato, Rosane Minghim, Haim Levkowitz. IEEE Transactions on Visualization and Computer Graphics, 2008

321 citations recorded by the source corpus.

How researchers see this paper

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Visualizing High-Dimensional Data: Advances in the Past Decade (2017, unknown): “The method introduced by Poco et al. [PEP ∗ 11b] focuses on enhancing the visual encoding and exploration usability of a 3D projection calculated by the Least-Square Projection [PNML08] algorithm.”
Visualizing the Hidden Activity of Artificial Neural Networks (2017, methodology): “Concerning computational cost, recent dimensionality reduction techniques are able to deal with hundreds of thousands of observations at interactive rates [33, 19].”
Hierarchical Stochastic Neighbor Embedding (2016, methodology): “…MNIST dataset produced with our approach compared to those created by non-linear techniques (tSNE and L-SNE [vdMH08]) and hybrid techniques (LSP [PNML08], Piecewise-LSP [PEP ∗ 11], created by the Pro-jection Explorer tool [POM07], and LAMP [JPC ∗ 11] created by the Projection Analyzer tool ∗∗ ).”
Toward a Quantitative Survey of Dimension Reduction Techniques (2019, methodology): “Least Square Projection ( LSP ) [86] and its faster version Piecewise Least Square Projection ( PLSP ) [87] use least squares approximations.”
Approximated and User Steerable tSNE for Progressive Visual Analytics (2015, unknown): “[28], propose the use of a non-linear dimensionalityreduction algorithm on a small number of automaticallyselected control points.”