Dimension reduction by local principal component analysis
Neural Computation
Mixtures of probabilistic principal component analyzers
Neural Computation
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Adaptive nonlinear manifolds and their applications to pattern recognition
Information Sciences: an International Journal
IEEE Transactions on Neural Networks
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In this paper, a local distribution neural network is proposed for data clustering. This competing network is designed for non-stationary and evolving environment. It represents data by means of neurons (ellipsoids) arranged on a topology map. The local distribution is stored in ellipsoids, while the global topology information is preserving in the relationship between adjacent ellipsoids. With a self-adapting threshold strategy and iteratively learning for information of local distribution, the algorithm is operated in an incremental and on-line way. During implementation, The adopted metric is an improved Mahalanobis distance which considers the local distribution and implies the anisotropy on different vector basis. Hence it can be interpreted as an incremental version of Gaussian mixture model. Experiments both on artificial data and real-world data are carried out to show the performance of the proposed method.