  In this study, a novel machine learning algorithm, restricted Boltzmann
machine (RBM), is introduced. The algorithm is applied for the spectral
classification in astronomy. RBM is a bipartite generative graphical model with
two separate layers (one visible layer and one hidden layer), which can extract
higher level features to represent the original data. Despite generative, RBM
can be used for classification when modified with a free energy and a soft-max
function. Before spectral classification, the original data is binarized
according to some rule. Then we resort to the binary RBM to classify
cataclysmic variables (CVs) and non-CVs (one half of all the given data for
training and the other half for testing). The experiment result shows
state-of-the-art accuracy of 100%, which indicates the efficiency of the binary
RBM algorithm.
