A Distance Adaptive Embedding Method in Dimension Reduction
The distribution preservation is a challenge inthe dimension reduction methods. This paper proposes a distance adaptive embedding method (DAE). The DAE method includes the cosine similarity technology and a new distance transformation function. It has the characteristics of easy handling and strong similarity distinction. The DAE method can make small loss value and good cluster discrimination by using the new distance transformation function in the embedding.The experiment results show that the DAE method has a good performance in distribution preservation, better than the performance of the multidimensional scaling method.
Keywordsdimension reduction clustering distance adaptive embedding
Unable to display preview. Download preview PDF.
- 2.Szekely, E., Bruno, E., Maillet, S.M.: High-Dimensional Multimodal Distribution Embedding. In: IEEE International Conference on Data Mining Workshops, pp. 434–441 (2010)Google Scholar
- 3.Fodor, I.K.: A survey of dimension reduction techniques (2002)Google Scholar
- 4.Faloutsos, C., Lin, K.J.: FastMap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasetsGoogle Scholar
- 5.Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill (1983)Google Scholar
- 6.Osinski, S.: Dimensionality reduction techniques for research results clustering (2004)Google Scholar
- 7.Wu, X.T., Yan, D.Q.: Analysis and research on method of data dimensionality reduction. Application Research of Computers 26(8) (2009)Google Scholar