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Register a new userLearning Diagonal Gaussian Mixture Models and Incomplete Tensor Decompositions
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Added by
Jiawang Nie
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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This paper studies how to learn parameters in diagonal Gaussian mixture models. The problem can be formulated as computing incomplete symmetric tensor decompositions. We use generating polynomials to compute incomplete symmetric tensor decompositions and approximations. Then the tensor approximation method is used to learn diagonal Gaussian mixture models. We also do the stability analysis. When the first and third order moments are sufficiently accurate, we show that the obtained parameters for the Gaussian mixture models are also highly accurate. Numerical experiments are also provided.
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