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Erschienen in:
2016 | OriginalPaper | Buchkapitel
A Manifold Learning Algorithm Based on Incremental Tangent Space Alignment
verfasst von : Chao Tan, Genlin Ji
Erschienen in: Cloud Computing and Security
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Abstract
Manifold learning is developed to find the observed data’s low dimension embeddings in high dimensional data space. As a type of effective nonlinear dimension reduction method, it has been widely applied to data mining, pattern recognition and machine learning. However, most existing manifold learning algorithms work in a “batch” mode and cannot effectively process data collected sequentially (or data streams). In order to explore the intrinsic low dimensional manifold structures in data streams on-line or incrementally, in this paper we propose a new manifold Learning algorithm based on Incremental Tangent Space Alignment, LITSA for short. By constructing data points’ local tangent spaces to preserve local coordinates incrementally, we can accurately obtain the low dimensional global coordinates. Experiments on both synthetic and real datasets show that the proposed algorithm can achieve a more accurate low-dimensional representation of the data than state-of-the-art incremental algorithms.