Learning to Read Between the Lines: The Aspect Bernoulli Model
Ata Kabán, Ella Bingham, Teemu Hirsimäki · 2004
We present a novel probabilistic multiple cause model for binary observations. In contrast to other approaches, the model is linear and it infers reasons behind both observed and unobserved attributes with the aid of an explanatory variable. We exploit this distinctive feature of the method to automatically distinguish between attributes that are ‘off’ by content and those that are missing. Results on artificially corrupted binary images as well as the expansion of short text documents are given by way of demonstration.