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

arXiv: https://arxiv.org/abs/1707.07538 · HTML: https://mlsanity.com/paper/1707.07538/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2017.

## Built on by (most cited first, 6 of 6)

- [A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set](https://mlsanity.com/paper/2201.03349/) (2022)
- [An Efficient and Accurate Rough Set for Feature Selection, Classification and Knowledge Representation](https://mlsanity.com/paper/2201.00436/) (2021)
- [Spectral Self-supervised Feature Selection](https://mlsanity.com/paper/2407.09061/) (2024)
- [The Origin of Self-Attention: Pairwise Affinity Matrices in Feature Selection and the Emergence of Self-Attention](https://mlsanity.com/paper/2507.14560/) (2025)
- [High-Order Conditional Mutual Information Maximization for dealing with High-Order Dependencies in Feature Selection](https://mlsanity.com/paper/2207.08476/) (2022)
- [Self-Attention And Beyond the Infinite: Towards Linear Transformers with Infinite Self-Attention](https://mlsanity.com/paper/2603.00175/) (2026)

## Abstract (arXiv)

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.