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Title: Modified Locally Linear Embedding with Affine Transformation
Authors: Mehta, Kanika
Tyagi, Gajendra
Rao, Aashish
Kumar, Pardeep
Chauhan, Durg Singh
Keywords: Locally linear embedding
Affine transformations
Unsupervised learning
Issue Date: 2017
Publisher: Jaypee University of Information Technology, Solan, H.P.
Abstract: Dimensional reduction is a primary way to analyze and work with complex and large amount of multidimensional data by avoiding the effect of curse of dimensionality. This problem of constructing low dimensional embedding gains importance in number of fields like artificial intelligence, image processing, geographical research and lot more. In this paper, we introduce a modified locally linear embedding, an unsupervised learning algorithm that computes low dimensional data from complex high dimensional data using affine transformation and neighborhood preserving embedding. Unlike novel locally linear embedding, our method is affine invariant where each point is being represented by an affine combination of its neighboring points. At the end, we conduct the experiment to evaluate our proposed method and compare its performance with existing methods. Results show that our proposed method is unaffected by affine transformation, specifically shear while existing methods fail to produce correct results in case of shear.
Appears in Collections:Journal Articles

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