# Restricted Boltzmann Machine for Classification with Hierarchical Correlated Prior

arXiv: https://arxiv.org/abs/1406.3407 · HTML: https://mlsanity.com/paper/1406.3407/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2014.

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

- [From Digital Humanities to Quantum Humanities: Potentials and Applications](https://mlsanity.com/paper/2103.11825/) (2021)

## Abstract (arXiv)

Restricted Boltzmann machines (RBM) and its variants have become hot research topics recently, and widely applied to many classification problems, such as character recognition and document categorization. Often, classification RBM ignores the interclass relationship or prior knowledge of sharing information among classes. In this paper, we are interested in RBM with the hierarchical prior over classes. We assume parameters for nearby nodes are correlated in the hierarchical tree, and further the parameters at each node of the tree be orthogonal to those at its ancestors. We propose a hierarchical correlated RBM for classification problem, which generalizes the classification RBM with sharing information among different classes. In order to reduce the redundancy between node parameters in the hierarchy, we also introduce orthogonal restrictions to our objective function. We test our method on challenge datasets, and show promising results compared to competitive baselines.