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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 312057.0, MPI für biologische Kybernetik / Biologische Kybernetik
The Rate Adapting Poisson Model
for Information Retrieval and Object Recognition
Authors:Gehler, P.V.; Holub, A.D.; Welling, M.
Date of Publication (YYYY-MM-DD):2006-06
Title of Proceedings:Proceedings of the 23rd International Conference on Machine Learning
Start Page:337
End Page:344
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Probabilistic modelling of text data in the bag-of-
words representation has been dominated by
directed graphical models such as pLSI, LDA,
NMF, and discrete PCA. Recently, state of the
art performance on visual object recognition has
also been reported using variants of these models.
We introduce an alternative undirected
graphical model suitable for modelling count
data. This “Rate Adapting Poisson” (RAP)
model is shown to generate superior dimensionally
reduced representations for subsequent retrieval
or classification. Models are trained using
contrastive divergence while inference of latent
topical representations is efficiently achieved
through a simple matrix multiplication.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:3929
URL:http://homepage.kyb.local/bs/people/pgehler/rap/in...
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