对角高斯混合模型的学习与不完全张量分解
Learning Diagonal Gaussian Mixture Models and Incomplete Tensor Decompositions
摘要：
研究了对角混合高斯模型中参数的学习问题。该问题可以表述为计算不完全对称张量分解。我们使用生成多项式来计算不完全对称张量分解和近似。然后采用张量逼近方法学习对角高斯混合模型。我们还做了稳定性分析。当一阶和三阶矩足够精确时，我们证明了高斯混合模型的参数也是高度精确的。数值实验也提供。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
