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We propose a new embedding method, named Quantile-Quantile Embedding (QQE), for distribution transformation and manifold embedding with the ability to choose the embedding distribution.
H. Allen, “The storage to be provided in impounding reservoirs for municipal water supply,” Transactions of the American society of civil engineers , vol. 77, pp. 1539–1669, 1914
1914
Earlier work this paper cites.
S. Kullback and R. A. Leibler, “On information and sufficiency,” The annals of mathematical statistics , vol. 22, no. 1, pp. 79–86, 1951
1951
Earlier work this paper cites.
H. W. Kuhn, “The Hungarian method for the assignment problem,” Naval research logistics quarterly , vol. 2, no. 1-2, pp. 83–97, 1955
1955
Earlier work this paper cites.
T. S. Ferguson, Mathematical statistics: A decision theoretic approach . Academic press, 1967
1967
Earlier work this paper cites.
J. W. Sammon, “A nonlinear mapping for data structure analysis,” IEEE Transactions on computers , vol. 100, no. 5, pp. 401–409, 1969
1969
Earlier work this paper cites.
J. Edmonds and R. M. Karp, “Theoretical improvements in algorithmic efficiency for network flow problems,” Journal of the ACM (JACM) , vol. 19, no. 2, pp. 248–264, 1972
1972
Earlier work this paper cites.
E. Parzen, “Nonparametric statistical data modeling,” Journal of the American statistical association , vol. 74, no. 365, pp. 105–121, 1979
1979
Earlier work this paper cites.
H. Leon Harter, “Another look at plotting positions,” Communications in Statistics-Theory and Methods , vol. 13, no. 13, pp. 1613–1633, 1984
1984
Earlier work this paper cites.
G. S. Easton and R. E. McCulloch, “A multivariate generalization of quantile-quantile plots,” Journal of the American Statistical Association , vol. 85, no. 410, pp. 376–386, 1990
1990
Earlier work this paper cites.
F. S. Samaria and A. C. Harter, “Parameterisation of a stochastic model for human face identification,” in Proceedings of 1994 IEEE workshop on applications of computer vision . IEEE, 1994, pp. 138–142
1994
Earlier work this paper cites.
J. Möttönen and H. Oja, “Multivariate spatial sign and rank methods,” Journaltitle of Nonparametric Statistics , vol. 5, no. 2, pp. 201–213, 1995
1995
Earlier work this paper cites.
R. J. Hyndman and Y. Fan, “Sample quantiles in statistical packages,” The American Statistician , vol. 50, no. 4, pp. 361–365, 1996
1996
Earlier work this paper cites.
P. Chaudhuri, “On a geometric notion of quantiles for multivariate data,” Journal of the American Statistical Association , vol. 91, no. 434, pp. 862–872, 1996
1996
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
J. B. Tenenbaum, V. De Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science , vol. 290, no. 5500, pp. 2319–2323, 2000
2000
Earlier work this paper cites.
S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science , vol. 290, no. 5500, pp. 2323–2326, 2000
2000
Earlier work this paper cites.
B. Schölkopf, “The kernel trick for distances,” Advances in neural information processing systems , pp. 301–307, 2001
2001
Earlier work this paper cites.
L. K. Saul and S. T. Roweis, “Think globally, fit locally: unsupervised learning of low dimensional manifolds,” Journal of machine learning research , vol. 4, no. Jun, pp. 119–155, 2003
2003
Earlier work this paper cites.
G. E. Hinton and S. T. Roweis, “Stochastic neighbor embedding,” in Advances in neural information processing systems , 2003, pp. 857–864
2003
Earlier work this paper cites.
R. Serfling, “Nonparametric multivariate descriptive measures based on spatial quantiles,” Journal of statistical Planning and Inference , vol. 123, no. 2, pp. 259–278, 2004
2004
Cited alongside, same era.
J. I. Marden, “Positions and QQ plots,” Statistical Science , vol. 19, no. 4, pp. 606–614, 2004
2004
Cited alongside, same era.
A. Gretton, O. Bousquet, A. Smola, and B. Schölkopf, “Measuring statistical dependence with Hilbert-Schmidt norms,” in International conference on algorithmic learning theory . Springer, 2005, pp. 63–77
2005
Cited alongside, same era.
J. Nocedal and S. Wright, Numerical optimization . Springer Science & Business Media, 2006
2006
Cited alongside, same era.
J. A. Gubner, Probability and random processes for electrical and computer engineers . Cambridge University Press, 2006
2006
Cited alongside, same era.
F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823
2015
Later among the works it cites.
D. W. Scott, Multivariate density estimation: theory, practice, and visualization . John Wiley & Sons, 2015
2015
Later among the works it cites.
I. Goodfellow, Y. Bengio, and A. Courville, Deep learning . MIT press Cambridge, 2016
2016
Later among the works it cites.
Y. Ren, J. Zhu, J. Li, and Y. Luo, “Conditional generative moment-matching networks,” in Advances in Neural Information Processing Systems , 2016, pp. 2928–2936
2016
Later among the works it cites.
A. Loy, L. Follett, and H. Hofmann, “Variations of Q–Q plots: The power of our eyes!” The American Statistician , vol. 70, no. 2, pp. 202–214, 2016
2016
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alphaXiv is searching for related work…
2006
Cited alongside, same era.
A. Gretton, K. Borgwardt, M. Rasch, B. Schölkopf, and A. J. Smola, “A kernel method for the two-sample-problem,” in Advances in neural information processing systems , 2007, pp. 513–520
2007
Cited alongside, same era.
J. A. Lee and M. Verleysen, Nonlinear dimensionality reduction . Springer Science & Business Media, 2007
2007
Cited alongside, same era.
T. Hofmann, B. Schölkopf, and A. J. Smola, “Kernel methods in machine learning,” The annals of statistics , pp. 1171–1220, 2008
2008
Cited alongside, same era.
L. V. D. Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of machine learning research , vol. 9, no. Nov, pp. 2579–2605, 2008
2008
Cited alongside, same era.
M. A. Cox and T. F. Cox, “Multidimensional scaling,” in Handbook of data visualization . Springer, 2008, pp. 315–347
2008
Cited alongside, same era.
L. Van Der Maaten, “Learning a parametric embedding by preserving local structure,” in Artificial Intelligence and Statistics , 2009, pp. 384–391
2009
Cited alongside, same era.
Later among the works it cites.
R. W. Oldford, “Self-calibrating quantile–quantile plots,” The American Statistician , vol. 70, no. 1, pp. 74–90, 2016
2016
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Later among the works it cites.
J. N. Kather, C.-A. Weis, F. Bianconi, S. M. Melchers, L. R. Schad, T. Gaiser, A. Marx, and F. G. Zöllner, “Multi-class texture analysis in colorectal cancer histology,” Scientific reports , vol. 6, no. 1, pp. 1–11, 2016
2016
Later among the works it cites.
2018
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
Q. Qian, L. Shang, B. Sun, J. Hu, H. Li, and R. Jin, “Softtriple loss: Deep metric learning without triplet sampling,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 6450–6458
2019
Later among the works it cites.
A. L. Cambridge, “AT&T laboratories Cambridge,” http://cam-orl.co.uk/facedatabase.html
2020
Closest in time.
2020
Closest in time.
B. Nguyen and B. De Baets, “Improved deep embedding learning based on stochastic symmetric triplet loss and local sampling,” Neurocomputing , vol. 402, pp. 209–219, 2020
2020
Closest in time.
M. Sikaroudi, B. Ghojogh, F. Karray, M. Crowley, and H. R. Tizhoosh, “Batch-incremental triplet sampling for training triplet networks using Bayesian updating theorem,” in Proceedings of the IEEE International Conference on Pattern Recognition (ICPR) . IEEE, 2020
2020
Closest in time.
S. Kalra, H. R. Tizhoosh, S. Shah, C. Choi, S. Damaskinos, A. Safarpoor, S. Shafiei, M. Babaie, P. Diamandis, C. J. Campbell et al. , “Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence,” NPJ digital medicine , vol. 3, no. 1, pp. 1–15, 2020
2020
Closest in time.
B. Ghojogh, M. Sikaroudi, S. Shafiei, H. R. Tizhoosh, F. Karray, and M. Crowley, “Fisher discriminant triplet and contrastive losses for training siamese networks,” in 2020 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2020, pp. 1–7
2020
Closest in time.