Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach
This addresses robustness in feature selection for computer vision applications, offering a novel method that is not incremental.
The paper tackles the problem of feature selection lacking robustness across heterogeneous datasets by proposing a probabilistic latent graph-based ranking algorithm that considers all feature subsets as paths on a graph. Results show it achieves the highest performance on ten benchmarks, setting a new state of the art with strong robustness.
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.