Assessment of dimensionality reduction based on communication channel model; Application to immersive information visualization

Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013

Volumn , Issue , 2013, Pages 1-6

  Babaee, Mohammadreza ^a   Datcu, Mihai ^b   Rigoll, Gerhard ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b GERMAN AEROSPACE CENTER DLR   (Germany)

Author keywords

Communication channel; Dimensionality Reduction; Immersive information Visualization; Quality Assessment

Indexed keywords

BIG DATA; CLUSTERING ALGORITHMS; COMMUNICATION CHANNELS (INFORMATION THEORY); DATA MINING; DATA VISUALIZATION; DIMENSIONALITY REDUCTION; ENTROPY; GEOMETRICAL OPTICS; INFORMATION SYSTEMS; MATRIX ALGEBRA; STOCHASTIC SYSTEMS; VISUALIZATION;

COMMUNICATION CHANNEL MODELING; DEGREE OF SIMILARITY; HIGH DIMENSIONAL DATA; HIGH DIMENSIONAL SPACES; HIGH-DIMENSIONAL IMAGES; INFORMATION VISUALIZATION; QUALITY ASSESSMENT; STOCHASTIC NEIGHBOR EMBEDDING;

DATA REDUCTION;

EID: 84893218068     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BigData.2013.6691726     Document Type: Conference Paper

References (18)

1. D. G. Lowe, "Object recognition from local scale-invariant features," in Computer vision, 1999. The proceedings of the seventh IEEE international conference on, vol. 2. Ieee, 1999, pp. 1150-1157.
2. R. Bahmanyar and M. Datcu, "Measuring the semantic gap based on a communication channel model," in Image Processing, IEEE International Conference on, 2013.
3. J. Choo, H. Lee, Z. Liu, J. Stasko, and H. Park, "An interactive visual testbed system for dimension reduction and clustering of large-scale high-dimensional data," in IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, 2013, pp. 865 402-865 402.
4. S. T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, no. 5500, pp. 2323-2326, 2000.
5. M. Belkin and P. Niyogi, "Laplacian eigenmaps for dimensionality reduction and data representation," Neural computation, vol. 15, no. 6, pp. 1373-1396, 2003.
6. G. Hinton and S. Roweis, "Stochastic neighbor embedding," Advances in neural information processing systems, vol. 15, pp. 833-840, 2002.
7. M. Bernstein, V. De Silva, J. C. Langford, and J. B. Tenenbaum, "Graph approximations to geodesics on embedded manifolds," Technical report, Department of Psychology, Stanford University, Tech. Rep., 2000.
8. M. Balasubramanian and E. L. Schwartz, "The isomap algorithm and topological stability," Science, vol. 295, no. 5552, pp. 7-7, 2002.
9. L. Van der Maaten, E. Postma, and H. Van den Herik, "Dimensionality reduction: A comparative review," Journal of Machine Learning Research, vol. 10, pp. 1-41, 2009.
10. L. Chen and A. Buja, "Local multidimensional scaling for nonlinear dimension reduction, graph layout and proximity analysis," Ph.D. dissertation, Citeseer, 2006.
11. -, "Local multidimensional scaling for nonlinear dimension reduction, graph drawing, and proximity analysis," Journal of the American Statistical Association, vol. 104, no. 485, pp. 209-219, 2009.
12. J. Venna and S. Kaski, "Local multidimensional scaling," Neural Networks, vol. 19, no. 6, pp. 889-899, 2006.
13. J. A. Lee and M. Verleysen, Nonlinear dimensionality reduction. Springer, 2007.
14. -, "Quality assessment of dimensionality reduction: Rank-based criteria," Neurocomputing, vol. 72, no. 7, pp. 1431-1443, 2009.
15. M. Chen and H. Jaenicke, "An information-theoretic framework for visualization," Visualization and Computer Graphics, IEEE Transactions on, vol. 16, no. 6, pp. 1206-1215, 2010.
16. J. A. Lee and M. Verleysen, "Rank-based quality assessment of nonlinear dimensionality reduction," in Proceedings of 16th European Symposium on Artificial Neural Networks, 2008, pp. 49-54.
17. K. E. van de Sande, T. Gevers, and C. G. Snoek, "Evaluating color descriptors for object and scene recognition," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 32, no. 9, pp. 1582-1596, 2010.
18. J. Chen, S. Shan, G. Zhao, X. Chen, W. Gao, and M. Pietikainen, "A robust descriptor based on weber's law," in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE, 2008, pp. 1-7.