Stabilizing and Simplifying Sharpened Dimensionality Reduction Using Deep Learning

[1]  P. J. Richards,et al.  Gaia Data Release 3. Summary of the content and survey properties , 2022, Astronomy & Astrophysics.

[2]  N. Xu,et al.  CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron , 2022, Pattern Recognit..

[3]  B. Tinsley.  EVOLUTION OF THE STARS AND GAS IN GALAXIES. , 2022, 2203.02041.

[4]  A. Telea,et al.  MING: An interpretative support method for visual exploration of multidimensional data , 2021, Inf. Vis..

[5]  Tao Jiang,et al.  LCU-Net: A novel low-cost U-Net for environmental microorganism image segmentation , 2021, Pattern Recognit..

[6]  Xirong Li,et al.  GasHis-Transformer: A multi-scale visual transformer approach for gastric histopathological image detection , 2021, Pattern Recognit..

[7]  Xin Zhao,et al.  Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches , 2020, Journal of X-ray science and technology.

[8]  Martin Becker,et al.  Robust dimensionality reduction for data visualization with deep neural networks , 2020, Graph. Model..

[9]  Andreas Kerren,et al.  Toward a Quantitative Survey of Dimension Reduction Techniques , 2019, IEEE Transactions on Visualization and Computer Graphics.

[10]  Luis Gustavo Nonato,et al.  Multidimensional Projection for Visual Analytics: Linking Techniques with Distortions, Tasks, and Layout Enrichment , 2019, IEEE Transactions on Visualization and Computer Graphics.

[11]  Alexandru Telea,et al.  Deep learning multidimensional projections , 2019, Inf. Vis..

[12]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection , 2018, J. Open Source Softw..

[13]  John F. Canny,et al.  T-SNE-CUDA: GPU-Accelerated T-SNE and its Applications to Modern Data , 2018, 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD).

[14]  Ulrik Brandes,et al.  Quality Metrics for Information Visualization , 2018, Comput. Graph. Forum.

[15]  Elmar Eisemann,et al.  GPGPU Linear Complexity t-SNE Optimization , 2018, IEEE Transactions on Visualization and Computer Graphics.

[16]  et al,et al.  Gaia Data Release 2 , 2018, Astronomy & Astrophysics.

[17]  U. Munari,et al.  The GALAH Survey: Second Data Release , 2018, 1804.06041.

[18]  Abien Fred Agarap.  Deep Learning using Rectified Linear Units (ReLU) , 2018, ArXiv.

[19]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.

[20]  Paulo E. Rauber,et al.  Projections as visual aids for classification system design , 2017, Inf. Vis..

[21]  Jie Li,et al.  A survey of dimensionality reduction techniques based on random projection , 2017, ArXiv.

[22]  Valerio Pascucci,et al.  Visualizing High-Dimensional Data: Advances in the Past Decade , 2017, IEEE Transactions on Visualization and Computer Graphics.

[23]  Alexandru Telea,et al.  CUBu: Universal Real-Time Bundling for Large Graphs , 2016, IEEE Transactions on Visualization and Computer Graphics.

[24]  Martin Wattenberg,et al.  How to Use t-SNE Effectively , 2016 .

[25]  Elmar Eisemann,et al.  Hierarchical Stochastic Neighbor Embedding , 2016, Comput. Graph. Forum.

[26]  Elmar Eisemann,et al.  Approximated and User Steerable tSNE for Progressive Visual Analytics , 2015, IEEE Transactions on Visualization and Computer Graphics.

[27]  Rosane Minghim,et al.  Explaining Neighborhood Preservation for Multidimensional Projections , 2015, CGVC.

[28]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[29]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[30]  J. Cunningham,et al.  Linear dimensionality reduction: survey, insights, and generalizations , 2014, J. Mach. Learn. Res..

[31]  Alberto D. Pascual-Montano,et al.  A survey of dimensionality reduction techniques , 2014, ArXiv.

[32]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[33]  Paul M. Brunet,et al.  The Gaia mission , 2013, 1303.0303.

[34]  Christophe Hurter,et al.  Graph Bundling by Kernel Density Estimation , 2012, Comput. Graph. Forum.

[35]  Luis Gustavo Nonato,et al.  Local Affine Multidimensional Projection , 2011, IEEE Transactions on Visualization and Computer Graphics.

[36]  Paulo Cortez,et al.  Modeling wine preferences by data mining from physicochemical properties , 2009, Decis. Support Syst..

[37]  Haim Levkowitz,et al.  Least Square Projection: A Fast High-Precision Multidimensional Projection Technique and Its Application to Document Mapping , 2008, IEEE Transactions on Visualization and Computer Graphics.

[38]  E. Massera,et al.  On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario , 2008 .

[39]  Jing Wang,et al.  MLLE: Modified Locally Linear Embedding Using Multiple Weights , 2006, NIPS.

[40]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[41]  Rosane Minghim,et al.  Text Map Explorer: a Tool to Create and Explore Document Maps , 2006, Tenth International Conference on Information Visualisation (IV'06).

[42]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[43]  T. Lumley,et al.  PRINCIPAL COMPONENT ANALYSIS AND FACTOR ANALYSIS , 2004, Statistical Methods for Biomedical Research.

[44]  D. Donoho,et al.  Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[45]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[46]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[47]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[48]  I-Cheng Yeh,et al.  Modeling of strength of high-performance concrete using artificial neural networks , 1998 .

[49]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[50]  Olvi L. Mangasarian,et al.  Nuclear feature extraction for breast tumor diagnosis , 1993, Electronic Imaging.

[51]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[52]  Joseph L. Zinnes,et al.  Theory and Methods of Scaling. , 1958 .

[53]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[54]  J. Roerdink,et al.  SDR-NNP: Sharpened Dimensionality Reduction with Neural Networks , 2022, VISIGRAPP.

[55]  Alexandru Telea,et al.  Self-supervised Dimensionality Reduction with Neural Networks and Pseudo-labeling , 2021, VISIGRAPP.

[56]  Laurens van der Maaten,et al.  Accelerating t-SNE using tree-based algorithms , 2014, J. Mach. Learn. Res..

[57]  Christian Widmer,et al.  Tapkee: an efficient dimension reduction library , 2013, J. Mach. Learn. Res..

[58]  Daniel Engel,et al.  A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and Visualization , 2011, VLUDS.

[59]  Eric O. Postma,et al.  Dimensionality Reduction: A Comparative Review , 2008 .

[60]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[61]  Jarkko Venna,et al.  Visualizing gene interaction graphs with local multidimensional scaling , 2006, ESANN.

[62]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

[63]  Joshua B. Tenenbaum,et al.  Sparse multidimensional scaling using land-mark points , 2004 .

[64]  张振跃,et al.  Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment , 2004 .

[65]  Teuvo Kohonen,et al.  Self-Organizing Maps, Second Edition , 1997, Springer Series in Information Sciences.

[66]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[67]  V. A. Epanechnikov.  Non-Parametric Estimation of a Multivariate Probability Density , 1969 .