A CONSENSUS-BASED DECENTRALIZED EM FOR A MIXTURE OF FACTOR ANALYZERS

We consider the problem of decentralized learning of a target appearance manifold using a network of sensors. Sensor nodes observe an object from different aspects and then, in an unsupervised and distributed manner, learn a joint statistical model for the data manifold. We employ a mixture of factor analyzers (MFA) model, approximating a potentially nonlinear manifold. We derive a consensus-based decentralized expectation maximization (EM) algorithm for learning the parameters of the mixture densities and mixing probabilities. A simulation example demonstrates the efficacy of the algorithm.