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Constrained Nonnegative Tensor Factorization for Clustering
2010 Ninth International Conference on Machine Learning and Applications, 2010Constrained clustering through matrix factorization has been shown to largely improve clustering accuracy by incorporating prior knowledge into the factorization process. Although it has been well studied, none of them deal with constrained multi-way data factorization. Multi-way data or Tensors are encoded as high-order data structures.
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Nonnegative Tensor Factorization with Smoothness Constraints
2008Nonnegative Tensor Factorization (NTF) is an emerging technique in multidimensional signal analysis and it can be used to find parts-based representations of high-dimensional data. In many applications such as multichannel spectrogram processing or multiarray spectra analysis, the unknown features have locally smooth temporal or spatial structure.
Rafal Zdunek, Tomasz M. Rutkowski
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Sparse Nonnegative Tensor Factorization and Completion With Noisy Observations
IEEE Transactions on Information Theory, 2022Xiongjun Zhang, Michael Ng
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Finding the Largest Eigenvalue of a Nonnegative Tensor
SIAM Journal on Matrix Analysis and Applications, 2010Michael Ng, Liqun Qi, Guanglu Zhou
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An always convergent algorithm for the largest eigenvalue of an irreducible nonnegative tensor
Journal of Computational and Applied Mathematics, 2010Guanglu Zhou, Nur Fadhilah Ibrahim
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