Classification using residual vector quantization

Residual vector quantization;Bayesian framework;Markov structure;1-Nearest neighbor

Ali Khan, Syed Irteza ; Barnes, Chritopher F. Anderson, David V. Electrical and Computer Engineering Lanterman, Aaron D. Howard, Ayanna M. Vela, Patricio A. Goldsman, David M. ; Barnes, Chritopher F.

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: Residual vector quantization;    Bayesian framework;    Markov structure;    1-Nearest neighbor;   

Others  :  https://smartech.gatech.edu/bitstream/1853/50300/1/ALIKHAN-DISSERTATION-2013.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Residual vector quantization (RVQ) is a 1-nearest neighbor (1-NN) type of technique. RVQ is a multi-stage implementation of regular vector quantization. An input is successively quantized to the nearest codevector in each stage codebook. In classification, nearest neighbor techniques are very attractive since these techniques very accurately model the ideal Bayes class boundaries. However, nearest neighbor classification techniques require a large size of representative dataset. Since in such techniques a test input is assigned a class membership after an exhaustive search the entire training set, a reasonably large training set can make the implementation cost of the nearest neighbor classifier unfeasibly costly. Although, the k-d tree structure offers a far more efficient implementation of 1-NN search, however, the cost of storing the data points can become prohibitive, especially in higher dimensionality.RVQ also offers a nice solution to a cost-effective implementation of 1-NN-based classification. Because of the direct-sum structure of the RVQ codebook, the memory and computational of cost 1-NN-based system is greatly reduced. Although, as compared to an equivalent 1-NN system, the multi-stage implementation of the RVQ codebook compromises the accuracy of the class boundaries, yet the classification error has been empirically shown to be within 3% to 4% of the performance of an equivalent 1-NN-based classifier.