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Results 1-4 of 4 (Search time: 0.043 seconds).
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  • A Survey on Tensor Techniques and Applications in Machine Learning

    • Authors: Ji, Y.;  Advisor: -;  Participants: Wang, Q.; Li, X.; Liu, J. (2019)

  • This survey gives a comprehensive overview of tensor techniques and applications in machine learning. Tensor represents higher order statistics. Nowadays, many applications based on machine learning algorithms require a large amount of structured high-dimensional input data. As the set of data increases, the complexity of these algorithms increases exponentially with the increase of vector size. Some scientists found that using tensors instead of the original input vectors can effectively solve these high-dimensional problems. This survey introduces the basic knowledge of tensor, including tensor operations, tensor decomposition, some tensor-based algorithms, and some applications of tensor in machine learning and deep learning for those who are interested in learning tensors. The t...
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  • Property-Constrained Dual Learning for Video Summarization

    • Authors: Zhao, B.;  Advisor: -;  Participants: Li, X.; Lu, X. (2019)

  • Video summarization is the technique to condense large-scale videos into summaries composed of key-frames or keyshots so that the viewers can browse the video content efficiently. Recently, supervised approaches have achieved great success by taking advantages of recurrent neural networks (RNNs). Most of them focus on generating summaries by maximizing the overlap between the generated summary and the ground truth. However, they neglect the most critical principle, i.e., whether the viewer can infer the original video content from the summary. As a result, existing approaches cannot preserve the summary quality well and usually demand large amounts of training data to reduce overfitting. In our view, video summarization has two tasks, i.e., generating summaries from videos and inferri...
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  • Power Allocation Schemes Based on Machine Learning for Distributed Antenna Systems

    • Authors: Liu, Y.;  Advisor: -;  Participants: He, C.; Li, X.; Zhang, C.; Tian, C. (2019)

  • In this paper, we investigate the great potential of the combination of machine learning technology and wireless communications. Currently, many researchers have proposed various optimization algorithms on resource allocation for distributed antenna systems (DASs). However, the existing methods are mostly hard to implement because of their high computational complexity. In this paper, a new system model for machine learning is considered for the scenario of DAS, which is more practical with its low computational complexity. We utilize the k-nearest neighbor (k-NN) algorithm based on the database of a traditional sub-gradient iterative method to get a power allocation scheme for DAS. The simulation results show that our k-NN algorithm can also obtain the power distribution scheme whi...
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  • Realization of a Hybrid Locally Connected Extreme Learning Machine With DeepID for Face Verification

    • Authors: Wong, S. Y.;  Advisor: -;  Participants: Yap, K. S.; Zhai, Q.; Li, X. (2019)

  • Most existing state-of-the-art deep learning algorithms discover sophisticated representations in huge datasets using convolutional neural networks (CNNs) that mainly adopt backpropagation (BP) algorithm as the backbone for training the face recognition problems. However, since decades ago, BP has been debated for causing trivial issues such as iterative gradient-descent operation, slow convergence rate, local minima, intensive human intervention, exhaustive computation, time-consuming, and so on. On the other hand, a competitive machine learning algorithm called extreme learning machine (ELM) emerged with extreme fast implementation and simple in theory has overcome the challenges faced by BP. The ELM advocates the convergence of machine learning and biological learning for pervasi...
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