  In the last decades the estimation of the intrinsic dimensionality of a
dataset has gained considerable importance. Despite the great deal of research
work devoted to this task, most of the proposed solutions prove to be
unreliable when the intrinsic dimensionality of the input dataset is high and
the manifold where the points lie is nonlinearly embedded in a higher
dimensional space. In this paper we propose a novel robust intrinsic
dimensionality estimator that exploits the twofold complementary information
conveyed both by the normalized nearest neighbor distances and by the angles
computed on couples of neighboring points, providing also closed-forms for the
Kullback-Leibler divergences of the respective distributions. Experiments
performed on both synthetic and real datasets highlight the robustness and the
effectiveness of the proposed algorithm when compared to state of the art
methodologies.
