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| December 5
| December 5
| Arijit Biswas
| Arijit Biswas
| TBA
| Distance Learning Using the Triangle Inequality for Semi-supervised Clustering
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The convolutional neural network based deep networks recently improve image classification accuracy significantly over the state-of-the-art vision approaches. I will go through what the successful deep convolutional neural net looks like, why it is again popular now and on-going deep net research in other research groups. I will mostly go through the successful instance of deep convolutional neural net tuned by Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton, published in NIPS 2012.
The convolutional neural network based deep networks recently improve image classification accuracy significantly over the state-of-the-art vision approaches. I will go through what the successful deep convolutional neural net looks like, why it is again popular now and on-going deep net research in other research groups. I will mostly go through the successful instance of deep convolutional neural net tuned by Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton, published in NIPS 2012.
===Distance Learning Using the Triangle Inequality for Semi-supervised Clustering===
Speaker: [http://www.umiacs.umd.edu/~arijit/ Arijit Biswas] -- Date: December 5, 2013
Success of semi-supervised clustering algorithms depends on how effectively supervision can be propagated to the unsupervised data. We propose a method for modifying all pairwise image distances when must-link or can't-link pairwise constraints are provided for only a few image pairs. These distances are used for clustering images. First, we formulate a brute-force Quadratic Programming (QP) method that modifies the distances such that the total change in distances is minimized but the final distances obey the triangle inequality. Then we propose a much faster version of the QP that can be applied to large datasets by enforcing only a selected subset of the inequalities. We prove that this still ensures that key qualitative properties of the distances are correctly computed. We run experiments on face, leaf and video image clustering and show that our proposed approach outperforms state-of-the-art methods for constrained clustering.
