Abstract
In this paper, we propose a novel linear dimensionality reduction algorithm, called Orthogonal Projection Analysis (OPA), from a gradient field perspective. Our approach is based on the following two criteria. First, the linear map should preserve the metric of the ambient space, which is based on the assumption that the metric of the ambient space is reliable. The second is the well-known smoothness criterion which is critical for clustering. Interestingly, gradient field is a natural tool to connect to these two requirements. We give a continuous objective function based on gradient fields and discuss how to discretize it by using tangent space. We also show the geometric meaning of our approach, which is requiring the gradient field as orthogonal as possible to the tangent spaces. The experimental results have demonstrated the effectiveness of our proposed approach.
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Lin, B., Zhang, C., He, X. (2012). Orthogonal Projection Analysis. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_1
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DOI: https://doi.org/10.1007/978-3-642-31919-8_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31918-1
Online ISBN: 978-3-642-31919-8
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