Rate-distortion bound for joint compression and classification

Summary form only given. Rate-distortion theory is applied to the problem of joint compression and classification. A Lagrangian distortion measure is used to consider both the Euclidean error in reconstructing the original data as well as the classification performance. The bound is calculated based on an alternating-minimization procedure, representing an extension of the Blahut-Arimoto algorithm. A hidden Markov model (HMM) source was considered as an example application and the objective is to quantize the source outputs and estimate the underlying HMM state sequence. Bounds on the minimum rate are required was presented to achieve desired average distortion on signal reconstruction and state-estimation accuracy.