Dichotomy Between Clustering Performance and Minimum Distortion in Piecewise-Dependent-Data (PDD) Clustering
| Type of publication: | Idiap-RR |
| Citation: | lapidot-rr02-48 |
| Number: | Idiap-RR-48-2002 |
| Year: | 2002 |
| Institution: | IDIAP and Ben-Gurion University of the Negev, Israel |
| Address: | Martigny, Switzerland |
| Note: | accepted for publication in IEEE Signal Processing Letters |
| Abstract: | In many signal such speech, bio-signals, protein chains, etc. there is a dependency between consecutive vectors. As the dependency is limited in duration such data can be called as Piecewise-Dependent- Data (PDD). In clustering it is frequently needed to minimize a given distance function. In this paper we will show that in PDD clustering there is a contradiction between the desire for high resolution (short segments and low distance) and high accuracy (long segments and high distortion,',','), i.e. meaningful clustering. |
| Userfields: | ipdinar={2002}, ipdmembership={speech}, language={English}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Crossref by |
lapidot-rr-02-48b |
| Added by: | [UNK] |
| Total mark: | 0 |
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