# ELBD: Efficient score algorithm for feature selection on latent variables of VAE

arXiv: https://arxiv.org/abs/2111.08493 · HTML: https://mlsanity.com/paper/2111.08493/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 15 Nov 2021.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Improving Variational Inference with Inverse Autoregressive Flow](https://mlsanity.com/paper/1606.04934/) (2016)
- [NVAE: A Deep Hierarchical Variational Autoencoder](https://mlsanity.com/paper/2007.03898/) (2020)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Preventing Posterior Collapse with delta-VAEs](https://mlsanity.com/paper/1901.03416/) (2019)
- [NeVAE: A Deep Generative Model for Molecular Graphs](https://mlsanity.com/paper/1802.05283/) (2018)
- [Deep Generative Video Compression](https://mlsanity.com/paper/1810.02845/) (2018)
- [Infinite Feature Selection: A Graph-based Feature Filtering Approach](https://mlsanity.com/paper/2006.08184/) (2020)
- [MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders](https://mlsanity.com/paper/1901.01498/) (2019)

## Built on by (most cited first, 1 of 1)

- [VSCOUT: A Hybrid Variational Autoencoder Approach to Outlier Detection in High-Dimensional Retrospective Monitoring](https://mlsanity.com/paper/2601.20830/) (2026)

## Abstract (arXiv)

In this paper, we develop the notion of evidence lower bound difference (ELBD), based on which an efficient score algorithm is presented to implement feature selection on latent variables of VAE and its variants. Further, we propose weak convergence approximation algorithms to optimize VAE related models through weighing the ``more important" latent variables selected and accordingly increasing evidence lower bound. We discuss two kinds of different Gaussian posteriors, mean-filed and full-covariance, for latent variables, and make corresponding theoretical analyses to support the effectiveness of algorithms. A great deal of comparative experiments are carried out between our algorithms and other 9 feature selection methods on 7 public datasets to address generative tasks. The results provide the experimental evidence of effectiveness of our algorithms. Finally, we extend ELBD to its generalized version, and apply the latter to tackling classification tasks of 5 new public datasets with satisfactory experimental results.