  Feature selection is playing an increasingly significant role with respect to
many computer vision applications spanning from object recognition to visual
object tracking. However, most of the recent solutions in feature selection are
not robust across different and heterogeneous set of data. In this paper, we
address this issue proposing a robust probabilistic latent graph-based feature
selection algorithm that performs the ranking step while considering all the
possible subsets of features, as paths on a graph, bypassing the combinatorial
problem analytically. An appealing characteristic of the approach is that it
aims to discover an abstraction behind low-level sensory data, that is,
relevancy. Relevancy is modelled as a latent variable in a PLSA-inspired
generative process that allows the investigation of the importance of a feature
when injected into an arbitrary set of cues. The proposed method has been
tested on ten diverse benchmarks, and compared against eleven state of the art
feature selection methods. Results show that the proposed approach attains the
highest performance levels across many different scenarios and difficulties,
thereby confirming its strong robustness while setting a new state of the art
in feature selection domain.
