非表示:
キーワード:
-
要旨:
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.
