Using Tensor Decompositions to Learn Semantic Mappings from Data Streams – The problem of recovering a single vector of a given point from a tensor of vectors is commonly encountered in data mining. This has led to many opportunities for data processing in the form of learning matrix completion (MC) algorithms. While MC algorithms in the literature exploit a non-linearity in the learning procedure, they do not take into account temporal dependencies. Inspired by recent advances in data mining, we propose the efficient learning algorithm CMC that combines linear and non-linearity in an approximate model search over the tensor of vectors. Our algorithm is an extension of MC algorithm, CMC (Chang et al., 2016), which is based on a non-linearity constraint that is a covariance relation between the tensor of vectors and its matrix. CMC allows us to compute the exact point-to-point matrix by computing its rank. Experiments on real datasets demonstrate CMC algorithm outperforms MC algorithms on several benchmark datasets.

The proposed method of using Submodular Maximization (SSM) is a basic framework for solving optimization problems. However, its computational complexity and time complexity (i.e., its computational complexity) are high. In this work, we provide a new computational study on its theoretical properties to investigate the performance of SSM from solving the optimization problems with large dimensions. To evaluate the performance of SSM, we propose a new algorithm called Submodular Maximization, which is based on the sub-sampling criterion which is a well-known criterion. The proposed algorithm is shown to be more robust than submodular optimization in solving small problems with a small number of solutions. The experimental results show that the proposed algorithm can be used for the large dimension optimization problems. The experimental results show that the proposed method outperforms the others on the optimization problems.

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# Using Tensor Decompositions to Learn Semantic Mappings from Data Streams

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Cascaded Submodular MaximizationThe proposed method of using Submodular Maximization (SSM) is a basic framework for solving optimization problems. However, its computational complexity and time complexity (i.e., its computational complexity) are high. In this work, we provide a new computational study on its theoretical properties to investigate the performance of SSM from solving the optimization problems with large dimensions. To evaluate the performance of SSM, we propose a new algorithm called Submodular Maximization, which is based on the sub-sampling criterion which is a well-known criterion. The proposed algorithm is shown to be more robust than submodular optimization in solving small problems with a small number of solutions. The experimental results show that the proposed algorithm can be used for the large dimension optimization problems. The experimental results show that the proposed method outperforms the others on the optimization problems.