Local Affine Multidimensional Projection
By Paul Joia, D. Coimbra, J. Cuminato, Fernando Vieira Paulovich, Luis Gustavo Nonato. IEEE Transactions on Visualization and Computer Graphics, 2011
299 citations recorded by the source corpus.
Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/d5de7f65822e1d79d03fe010d9b2144cde2780a3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Graphics and Computer-Aided Design
- Yingcai Wu
- Mengchen Liu
- Alexandru Cristian Telea
- L. V. D. van der Maaten
- Weiwei Cui
- Nina Sumiko Tomita Hirata
- Ronghua Liang
- Guodao Sun
In Computer Vision and Pattern Recognition
- Shixia Liu
- Boudewijn P. F. Lelieveldt
- Elmar Eisemann
- Anna Vilanova
- Nicola Pezzotti
- Thomas Höllt
- Mengchen Liu
- Yingcai Wu
In Human-Computer Interaction
- Yingcai Wu
- Alexandru Cristian Telea
- Alexandre Xavier Falcão
- L. V. D. van der Maaten
- Nina Sumiko Tomita Hirata
- Shixia Liu
- Mateus Espadoto
Citations in the corpus
- A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges (2013, methodology): “Local affine multidimensional projection (LAMP)([62]) is a new projection method based on an orthogonal mapping theory for handling high dimensional data.”
- A survey on information visualization: recent advances and challenges (2014, background): “approaches focuses on exploring new projection techniques [72,140] to reveal unexpected data distributions or integrating multiple geometric approaches to avoid limitations of”
- Approximated and User Steerable tSNE for Progressive Visual Analytics (2015, background): “It indicates the change in position of the low-dimensional points for each step of the gradient descent and is given by: The gradient descent can be seen as a N-body simulation [33], where each data-point exerts an attractive and a repulsive force on all the other points ( F attr i and F rep i ).”
- Toward a Quantitative Survey of Dimension Reduction Techniques (2019, methodology): “Normalized stress M : With values in [0, 1], with 0 being the best, this measures the preservation of point-pairwise distances from D to P (D) [73].”
- Visualizing the Hidden Activity of Artificial Neural Networks (2017, background): “Fortunately, preliminary experiments with dimensionality reduction techniques that are able to deal with hundreds of thousands of observations at interactive paces [33, 19] were also promising.”
- Deep learning multidimensional projections (2019, methodology): “The Shepard diagram is a scatterplot of the pairwise (Euclidean) distances between all points in P(D) versus the corresponding distances in D.(25) The closer the plot is to the main diagonal, the better overall distance preservation is.”