PAKDD 2015: Advances in Knowledge Discovery and Data Mining pp 646-658 | Cite as
An Incremental Local Distribution Network for Unsupervised Learning
Abstract
We present an Incremental Local Distribution Network (ILDN) for unsupervised learning, which combines the merits of matrix learning and incremental learning. It stores local distribution information in each node with covariant matrix and uses a vigilance parameter with statistical support to decide whether to extend the network. It has a statistics based merging mechanism and thus can obtain a precise and concise representation of the learning data called relaxation representation. Moreover, the denoising process based on data density makes ILDN robust to noise and practically useful. Experiments on artificial and real-world data in both “closed” and “open-ended” environment show the better accuracy, conciseness, and efficiency of ILDN over other methods.
Keywords
Incremental learning Matrix learning Relaxation representationPreview
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