Akademik Veri Yönetim Sistemi
7th International Conference on Computational Methods in Science and Engineering (ICCMSE), Rhodes, Yunanistan, 29 Eylül - 04 Ekim 2009, cilt.1504, ss.837-840
Scientists often face the challenge of extracting meaningful patterns from large amounts of high dimensional data such as digital images, brain scans and stellar spectra. Previous research suggests that space extension methods such as kernel methods coupled with dimensionality reduction can extract patterns that might not be clearly evident in the original data. In this work we first increase the dimensionality of the data vector to produce a multilinear array, then we decompose this array into binary outer products via a new multilinear array decomposition method. Results from approximation of digital images are encouraging.