MNIST Multiview Datasets
Introduced by Goyal et al. in Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
MNIST Multiview Datasets
MNIST is a publicly available dataset consisting of 70, 000 images of handwritten digits distributed over ten classes. We generated 2 four-view datasets where each view is a vector of R<sup>14 x 14</sup>:
- MNIST<sub>1</sub>: It is generated by considering 4 quarters of image as 4 views.
- MNIST<sub>2</sub>: It is generated by considering 4 overlapping views around the centre of images: this dataset brings redundancy between the views.
Related Papers:
Goyal, Anil, Emilie Morvant, Pascal Germain, and Massih-Reza Amini.
"Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters."
Neurocomputing, 358, 2019, pp. 81-92.
Link to the ArXiv version:
https://arxiv.org/abs/1808.05784
Published Version:
https://doi.org/10.1016/j.neucom.2019.04.072
Goyal, Anil, Emilie Morvant, and Massih-Reza Amini.
"Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization."
In International Symposium on Intelligent Data Analysis, pp. 124-136. Springer, Cham, 2018.
Link to the ArXiv version:
https://arxiv.org/abs/1805.10212
Published Version:
https://doi.org/10.1007/978-3-030-01768-2_11
Papers
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