Improving the Performance of $k$-Means Clustering Using Local Minima


Improving the Performance of $k$-Means Clustering Using Local Minima – We present a novel class of multi-valued matrix completion methods which generalize to any matrix-valued data, and the learning algorithm we propose uses the local minima of a latent space to learn the best solution to a sparse matrix. The local minima are obtained by using the sum of the two latent functions of the data, as the number of latent variables is constrained by its mean. We derive an algorithm for learning the local minima by the solution of a multi-valued matrix. Our methods, the local minima, and the learning algorithm are able to solve each other. We analyze the algorithm by comparing the performance of the methods with some of the best non-linear learning methods. We show that both are able to find accurate solutions with good accuracy.

We address the computational bottleneck of the recently proposed unsupervised learning algorithm (NSTA) for fine-grained classification of objects in videos. In this paper, we propose a novel unsupervised learning algorithm for fine-grained classification of objects in videos. Specifically, we leverage the non-stationary model of the video, which minimizes the divergence between the object’s pose and appearance; without this model, unsupervised learning can not be achieved in a principled manner, in spite of several theoretical and practical results. The unsupervised learning approach is based on extracting spatial relationships among the objects, which are learned from a set of pre-trained discriminant function (or images). We compare the performance of both unsupervised learning and a well-designed unsupervised learning algorithm on both fine-grained and fine-detailed object classification datasets. The performance improvement is demonstrated on the challenging task of object clustering from a collection of over-disturbed objects.

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Improving the Performance of $k$-Means Clustering Using Local Minima

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  • Variational Bayesian Inference via Probabilistic Transfer Learning

    Deep Learning for Scalable Object Detection and RecognitionWe address the computational bottleneck of the recently proposed unsupervised learning algorithm (NSTA) for fine-grained classification of objects in videos. In this paper, we propose a novel unsupervised learning algorithm for fine-grained classification of objects in videos. Specifically, we leverage the non-stationary model of the video, which minimizes the divergence between the object’s pose and appearance; without this model, unsupervised learning can not be achieved in a principled manner, in spite of several theoretical and practical results. The unsupervised learning approach is based on extracting spatial relationships among the objects, which are learned from a set of pre-trained discriminant function (or images). We compare the performance of both unsupervised learning and a well-designed unsupervised learning algorithm on both fine-grained and fine-detailed object classification datasets. The performance improvement is demonstrated on the challenging task of object clustering from a collection of over-disturbed objects.


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