  Large-scale undirected weighted networks are usually found in big
data-related research fields. It can naturally be quantified as a symmetric
high-dimensional and incomplete (SHDI) matrix for implementing big data
analysis tasks. A symmetric non-negative latent-factor-analysis (SNL) model is
able to efficiently extract latent factors (LFs) from an SHDI matrix. Yet it
relies on a constraint-combination training scheme, which makes it lack
flexibility. To address this issue, this paper proposes an unconstrained
symmetric nonnegative latent-factor-analysis (USNL) model. Its main idea is
two-fold: 1) The output LFs are separated from the decision parameters via
integrating a nonnegative mapping function into an SNL model; and 2) Stochastic
gradient descent (SGD) is adopted for implementing unconstrained model training
along with ensuring the output LFs nonnegativity. Empirical studies on four
SHDI matrices generated from real big data applications demonstrate that an
USNL model achieves higher prediction accuracy of missing data than an SNL
model, as well as highly competitive computational efficiency.
