Constructing a large node Chow-Liu tree based on frequent itemsets
We present a novel approach to construct a kind of tree belief network, in which the "nodes" are subsets of variables of dataset. We call this large node Chow-Liu tree (LNCLT). Similar to the Chow-Liu tree (1968), the LNCLT is also ideal for density estimation and classification applications. This technique uses the concept of "frequent itemsets" as found in the database literature to guide the construction of the LNCLT. Our LNCLT has a simpler structure while it maintains a good fitness over the dataset. We detail the theoretical formulation of our approach. Moreover, based on the MNIST hand-printed digit database, we conduct a series of digit recognition experiments to verify our approach. From the result we find that both recognition rate and density estimation accuracy are improved with the LNCLT structure.
