Vector quantization using information theoretic concepts
✍ Scribed by Tue Lehn-schiøler; Anant Hegde; Deniz Erdogmus; Jose C. Principe
- Book ID
- 106476123
- Publisher
- Springer Netherlands
- Year
- 2005
- Tongue
- English
- Weight
- 312 KB
- Volume
- 4
- Category
- Article
- ISSN
- 1567-7818
No coin nor oath required. For personal study only.
📜 SIMILAR VOLUMES
In this article we have generalized Dunn's index and the Davies-Bouldin index for cluster validation using graph structures, such as GG, RNG and MST. Unlike Dunn's index and the Davies-Bouldin index, the proposed indices are not sensitive to noisy points and are applicable to hyperspherical and stru
This paper describes the permutative vector quantization (PVQ) scheme as a special case of a more general structurally constrained vector quantization concept. This concept makes it possible to increase the vector dimensions beyond the technical bounds of conventional VQ and to exploit by these mean
In vector quantization (VQ), which is useful for the digital coding of large-volume signals such as for image or audio data, many conventional techniques had assumed that the input signal has a time-invariant probability distribution. However, in a real setting, since the stochastic nature of the en