Self-organizing maps, vector quantization, and mixture modeling Export

@article{citeulike:2878417, abstract = {Self-organizing maps are popular algorithms for unsupervised learning and data visualization. Exploiting the link between vector quantization and mixture modeling, we derive expectation-maximization (EM) algorithms for self-organizing maps with and without missing values. We compare self-organizing maps with the elastic-net approach and explain why the former is better suited for the visualization of high-dimensional data. Several extensions and improvements are discussed. As an illustration we apply a self-organizing map based on a multinomial distribution to market basket analysis}, author = {Heskes, T.}, booktitle = {Neural Networks, IEEE Transactions on}, citeulike-article-id = {2878417}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/72.963766}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=963766}, doi = {10.1109/72.963766}, journal = {Neural Networks, IEEE Transactions on}, keywords = {map, mkis}, number = {6}, pages = {1299--1305}, posted-at = {2009-02-01 23:44:54}, priority = {0}, title = {Self-organizing maps, vector quantization, and mixture modeling}, url = {http://dx.doi.org/10.1109/72.963766}, volume = {12}, year = {2001} }

TY - JOUR ID - citeulike:2878417 L3 - citeulike-article-id:2878417 N2 - Self-organizing maps are popular algorithms for unsupervised learning and data visualization. Exploiting the link between vector quantization and mixture modeling, we derive expectation-maximization (EM) algorithms for self-organizing maps with and without missing values. We compare self-organizing maps with the elastic-net approach and explain why the former is better suited for the visualization of high-dimensional data. Several extensions and improvements are discussed. As an illustration we apply a self-organizing map based on a multinomial distribution to market basket analysis IS - 6 JF - Neural Networks, IEEE Transactions on EP - 1305 TI - Self-organizing maps, vector quantization, and mixture modeling VL - 12 T2 - Neural Networks, IEEE Transactions on SP - 1299 KW - map KW - mkis AU - Heskes, T PY - 2001/// UR - http://dx.doi.org/10.1109/72.963766 ER -