On Recovering Latent Factors From Sampling And Firing Graph

TL;DR

This paper investigates whether it is possible to develop a general method to identify latent factor activations from perfect indicators in grid-based binary data, providing both theoretical insights and practical approaches.

Contribution

It offers a novel theoretical framework and practical procedures for recovering latent factors from binary grid data with perfect activation indicators.

Findings

Proposes a method for latent factor identification

Provides theoretical guarantees for the recovery process

Demonstrates effectiveness on simulated data

Abstract

Consider a set of latent factors whose observable effect of activation is caught on a measure space that appears as a grid of bits tacking value in $\{0, 1 \}$. This paper intend to deliver a theoretical and practical answer to the question: Given that we have access to a perfect indicator of the activation of latent factors that label a finite dataset of grid's activity, can we imagine a procedure to build a generic identificator of factor's activations ?