  We propose a new tensor factorization method, called the Sparse
Hierarchical-Tucker (Sparse H-Tucker), for sparse and high-order data tensors.
Sparse H-Tucker is inspired by its namesake, the classical Hierarchical Tucker
method, which aims to compute a tree-structured factorization of an input data
set that may be readily interpreted by a domain expert. However, Sparse
H-Tucker uses a nested sampling technique to overcome a key scalability problem
in Hierarchical Tucker, which is the creation of an unwieldy intermediate dense
core tensor; the result of our approach is a faster, more space-efficient, and
more accurate method. We extensively test our method on a real healthcare
dataset, which is collected from 30K patients and results in an 18th order
sparse data tensor. Unlike competing methods, Sparse H-Tucker can analyze the
full data set on a single multi-threaded machine. It can also do so more
accurately and in less time than the state-of-the-art: on a 12th order subset
of the input data, Sparse H-Tucker is 18x more accurate and 7.5x faster than a
previously state-of-the-art method. Even for analyzing low order tensors (e.g.,
4-order), our method requires close to an order of magnitude less time and over
two orders of magnitude less memory, as compared to traditional tensor
factorization methods such as CP and Tucker. Moreover, we observe that Sparse
H-Tucker scales nearly linearly in the number of non-zero tensor elements. The
resulting model also provides an interpretable disease hierarchy, which is
confirmed by a clinical expert.

Machine Learning;Numerical Analysis