Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach

TL;DR

This paper introduces a probabilistic graph-based feature selection method that considers all feature subsets as paths, improving robustness and performance across diverse computer vision tasks.

Contribution

It proposes a novel latent graph-based ranking algorithm that analytically bypasses combinatorial complexity and models feature relevancy as a latent variable, enhancing robustness.

Findings

Achieves highest performance on ten benchmarks

Outperforms eleven state-of-the-art methods

Demonstrates strong robustness across diverse scenarios

Abstract

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.