Rate-distortion bound for joint compression and classification

Yanting Dong, L. Carin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.
Original languageEnglish (US)
Title of host publicationData Compression Conference, 2003. Proceedings. DCC 2003
ISBN (Print)0-7695-1896-6
StatePublished - Mar 27 2003
Externally publishedYes
EventData Compression Conference, 2003. Proceedings. DCC 2003 - Snowbird, UT, USA
Duration: Mar 25 2003Mar 27 2003


ConferenceData Compression Conference, 2003. Proceedings. DCC 2003


  • Rate-distortion
  • Hidden Markov models
  • Distortion measurement
  • Lagrangian functions
  • Computer errors
  • State estimation
  • Signal reconstruction
  • Data compression


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