The deep Boltzmann machine is one of the deep learning models, and it has two layers. The first layer consists of observed data variables (visible units), and the second layer consists of latent variables (hidden units). The deep Boltzmann machine is an unsupervised and probabilistic generative model with a unidirectional connection between layers. DBM extracts highly sophisticated features for more complex tasks. DBM layers are organized as a bipartite graph, with odd layers on one side, even layers on another side.
DBM learns the features hierarchically from the raw data, and the features extracted in one layer are applied as hidden variables as input to the subsequent layer. It is mainly utilized for classification, dimensionality reduction problems, regression, and collaborative filtering. The application areas of the deep Boltzmann machine model are image recognition, collaborative filtering, feature learning, and topic modeling. Recent research on Deep Boltzmann Machine involves neuron image segmentation, shape completion, corrosion classification, to name a few.
• Deep Boltzmann Machine (DBM) utilizes Restricted Boltzmann Machine (RBM) as a learning module and has demonstrated the ability to learn the more complex intrinsic representations of the data.
• DBM is an unsupervised model that encompasses the multiple layers of hidden units and captures many layers of complex representations of input data in an undirected connection.
• It is a process of top-down feedback, incorporating uncertainty about inputs more effectively and training on unlabeled data.
• Even though the DBM model is appropriate for unsupervised training, it can also be used for fine-tuning a particular task in a supervised fashion.
• One of the major drawbacks of DBM is the high computational cost of inference.
• In terms of applications, Deep Boltzmann Machine has been widely used to solve various machine learning problems such as regression, classification, dimensionality reduction, time series modeling, and image co-filtering and achieved significant performance rates in various visual understanding tasks.