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 present a novel approach for learning deep neural networks (DNNs) on-the-fly. The approach addresses two distinct challenges: (1) is the DNN not only trained and optimized for all inputs at each time step, but also all layers are trained in all layers and learn to discriminate between inputs in a coherent representation; and (2) is the DNN trained on the learned representations of the input. The DNN training is accomplished by using a deep architecture and utilizes the data structure to capture the learned discriminative representation of the input, which is then used to train a DNN with the discriminative representation. Experiments on various challenging datasets demonstrate that our approach outperforms the state-of-the-art deep neural network architectures.

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

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  • Efficient Classification and Ranking of Multispectral Data Streams using Genetic Algorithms

    Multi-view Deep Reinforcement Learning with Dynamic CodingWe present a novel approach for learning deep neural networks (DNNs) on-the-fly. The approach addresses two distinct challenges: (1) is the DNN not only trained and optimized for all inputs at each time step, but also all layers are trained in all layers and learn to discriminate between inputs in a coherent representation; and (2) is the DNN trained on the learned representations of the input. The DNN training is accomplished by using a deep architecture and utilizes the data structure to capture the learned discriminative representation of the input, which is then used to train a DNN with the discriminative representation. Experiments on various challenging datasets demonstrate that our approach outperforms the state-of-the-art deep neural network architectures.


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