Deng Zhaohong

Robust maximum entropy clustering algorithm with its labeling for outliers

In this paper, a novel robust maximum entropy clustering algorithm RMEC, as the improved version of the maximum entropy algorithm MEC [2–4], is presented to overcome MECs drawbacks: very sensitive to outliers and uneasy to label them. Algorithm RMEC incorporates Vapniks ɛ – insensitive loss function and the new concept of weight factors into its objective function and consequently, its new update rules are derived according to the Lagrangian optimization theory. Compared with algorithm MEC, the main contributions of algorithm RMEC exit in its much better robustness to outliers and the fact that it can effectively label outliers in the dataset using the obtained weight factors. Our experimental results demonstrate its superior performance in enhancing the robustness and labeling outliers in the dataset.

Fuzzy kernel hyperball perceptron

Abstract In this paper the novel fuzzy kernel hyperball perceptron is presented. The proposed method first maps the input data into a high-dimensional feature space using some Mercer kernel function. Then, the decision function for each class is derived by the learning rules of the fuzzy kernel hyperball perceptron. The fuzzy membership functions, which resolve unclassifiable zones among classes, are incorporated into its classification algorithm to further enhance the perceptron’s adaptability and classification accuracy effectively. Unlike SVM, the fuzzy kernel hyperball perceptron has no convergence problem and avoids solving so-called quadratic programming problem which often makes SVM ineffective for large data sets. Especially, unlike the classical SVMs, we can directly utilize the fuzzy kernel hyperball perceptrons to solve multiclass problems, without using any pairwise combination. Our experimental results demonstrate its effectiveness.