  The main goal of this paper is to explore latent topic analysis (LTA), in the
context of quantum information retrieval. LTA is a valuable technique for
document analysis and representation, which has been extensively used in
information retrieval and machine learning. Different LTA techniques have been
proposed, some based on geometrical modeling (such as latent semantic analysis,
LSA) and others based on a strong statistical foundation. However, these two
different approaches are not usually mixed. Quantum information retrieval has
the remarkable virtue of combining both geometry and probability in a common
principled framework. We built on this quantum framework to propose a new LTA
method, which has a clear geometrical motivation but also supports a
well-founded probabilistic interpretation. An initial exploratory
experimentation was performed on three standard data sets. The results show
that the proposed method outperforms LSA on two of the three datasets. These
results suggests that the quantum-motivated representation is an alternative
for geometrical latent topic modeling worthy of further exploration.

Machine Learning;Information Retrieval;Machine Learning