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Classification based on dimension transposition for high dimension data
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DOI：10.1007/s00500-006-0085-3.png)
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Abstract
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Based on Jordan Curve Theorem, a universal classification method called hyper surface classification (HSC) has recently been proposed. Experimental results are exciting, which show that in three-dimensional space, this method works fairly well in both accuracy and efficiency even for large size data up to 10(7). However, designing a number of new classifiers is needed with the growing of feature dimension. To solve the problem, a kind of efficient dimension transposition method that is suitable for HSC and without losing any essential information is put forward in this paper. The dimension transposition method rearrange all of the numerals in the higher dimensional data to lower dimensional data without changing each numeral, but only change their position according to some orders. The experiment shows that the method can classify high dimension data with high accuracy.
Keywords:
dimension transposition
SVM
HSC
Jordan Curve Theorem
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