Wentao Fan

TL;DR: A simple but fast DPeak, namely FastDPeak, 1 is proposed, which runs in about O ( n l o g ( n ) ) expected time in the intrinsic dimensionality and replaces density with kNN-density, which is computed by fast kNN algorithm such as cover tree, yielding huge improvement for density computations.

TL;DR: This paper focuses on the variational learning of finite Dirichlet mixture models and suggests that this approach has several advantages: first, the problem of over-fitting is prevented; furthermore, the complexity of the mixture model can be determined automatically and simultaneously with the parameters estimation as part of the Bayesian inference procedure.

TL;DR: The experimental results reported for both synthetic data and real-world challenging applications involving image categorization, automatic semantic annotation and retrieval show the ability of the approach to provide accurate models by distinguishing between relevant and irrelevant features without over- or under-fitting the data.

TL;DR: This paper proposes a novel unsupervised statistical approach for detecting network based attacks through finite generalized Dirichlet mixture models, in the context of Bayesian variational inference.

TL;DR: A principled approach for approximating the intractable model's posterior distribution by a tractable one is proposed-which is developed-such that all the involved mixture's parameters can be estimated simultaneously and effectively in a closed form.