Online churn detection on high dimensional cellular data using adaptive hierarchical trees

Abstract

We study online sequential logistic regression for churn detection in cellular networks when the feature vectors lie in a high dimensional space on a time varying manifold. We escape the curse of dimensionality by tracking the subspace of the underlying manifold using a hierarchical tree structure. We use the projections of the original high dimensional feature space onto the underlying manifold as the modified feature vectors. By using the proposed algorithm, we provide significant classification performance with significantly reduced computational complexity as well as memory requirement. We reduce the computational complexity to the order of the depth of the tree and the memory requirement to only linear in the intrinsic dimension of the manifold. We provide several results with real life cellular network data for churn detection.