Initializing subspace constrained Gaussian mixture models
A recent series of papers [1, 2, 3, 4] introduced subspace constrained Gaussian mixture models (SCGMM) and showed that SCGMM can very efficiently approximate full covariance Gaussian mixture models (FCGMM); a significant reduction in the number of parameters is achieved with little loss in the accuracy of the model. SCGMM were arrived at as a sequence of generalizations of diagonal covariance GMM. As an artifact of this process the initialization of SCGMM parameters in that work is complex, i.e., relies on best parameter settings of less general models. This paper overcomes this problem by showing how an FCGMM can be used to give a simple and direct initialization of an SCGMM. The initialization scheme is powerful enough that as the number of parameters in an SCGMM approaches that of an FCGMM (i.e., large SCGMM) further training of the SCGMM is unnecessary.