Konferenční příspěvek

Hora J.

Doktorandské dny 2007

Česká technika ČVUT

Praha

2007

978-80-01-03913-7

prof. Ing. Michal Haindl, DrSc. (ÚTIA - Oddělení rozpoznávání obrazů)

EM algorithm, distribution mixtures, cluster analysis, cathe

The EM algorithm has been used repeatedly to identify latent classes in categorical data by estimating finite distribution mixtures of product components. Unfortunately, the underlying mixtures are not uniquely identifiable and, moreover, the estimated mixture parameters are starting-point dependent. For this reason we use the latent class model only to define a set of ``elementary'' classes by estimating a mixture of a large number components. As such a mixture we use also an optimally smoothed kernel estimate. We propose a hierarchical ``bottom up'' cluster analysis based on unifying the elementary latent classes sequentially. The clustering procedure is controlled by minimum information loss criterion.