Nonnegative matrix factorization (NMF) is a popular dimension reduction
technique used for clustering by extracting latent features from highdimensional
data and is widely used for text mining. Several optimization
algorithms have been developed for NMF with different cost functions. In this
paper we evaluate the correntropy similarity cost function. Correntropy is a
nonlinear localized similarity measure which measures the similarity between
two random variables using entropy-based criterion, and is especially robust to
outliers. Some algorithms based on gradient descent have been used for
correntropy cost function, but their convergence is highly dependent on proper
initialization and step size and other parameter selection. The proposed general
multiplicative factorization algorithm uses the gradient descent algorithm with
adaptive step size to maximize the correntropy similarity between the data
matrix and its factorization. After devising the algorithm, its performance has
been evaluated for document clustering. Results were compared with
constrained gradient descent method using steepest descent and L-BFGS
methods. The simulations show that the performance of steepest descent and LBFGS
convergence are highly dependent on gradient descent step size which
depends on σ parameter of correntropy cost function. However, the
multiplicative algorithm is shown to be less sensitive to σ parameterand yields
better clustering results than other algorithms. The results demonstrate that
clustering performance measured by entropy and purity improve the clustering.
The multiplicative correntropy-based algorithm also shows less variation in
accuracy of document clusters for variable number of clusters. The convergence
of each algorithm is also investigated, and the experiments have shown that the
multiplicative algorithm converges faster than L-BFGS and steepest descent
method.
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