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Balázs Kégl, Adam Krzyzak, Tamás Linder, Kenneth Zeger, "Learning and Design of Principal Curves," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 3, pp. 281297, March, 2000.  
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@article{ 10.1109/34.841759, author = {Balázs Kégl and Adam Krzyzak and Tamás Linder and Kenneth Zeger}, title = {Learning and Design of Principal Curves}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {22}, number = {3}, issn = {01628828}, year = {2000}, pages = {281297}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.841759}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Learning and Design of Principal Curves IS  3 SN  01628828 SP281 EP297 EPD  281297 A1  Balázs Kégl, A1  Adam Krzyzak, A1  Tamás Linder, A1  Kenneth Zeger, PY  2000 KW  Learning systems KW  unsupervised learning KW  feature extraction KW  vector quantization KW  curve fitting KW  piecewise linear approximation. VL  22 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Abstract—Principal curves have been defined as “selfconsistent” smooth curves which pass through the “middle” of a
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