On Recovering Latent Factors From Sampling And Firing Graph
This addresses the challenge of identifying latent factors in binary data for researchers in machine learning and data analysis, but appears incremental as it builds on existing labeling methods.
The paper tackles the problem of recovering latent factors from binary grid observations given perfect activation labels, proposing a theoretical and practical procedure for building a generic identifier of factor activations.
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 ?