TY - GEN
T1 - Unsupervised learning by convex and conic coding
AU - Lee, D. D.
AU - Seung, Hyunjune Sebastian
PY - 1997
Y1 - 1997
N2 - Unsupervised learning algorithms based on convex and conic encoders are proposed. The encoders find the closest convex or conic combination of basis vectors to the input. The learning algorithms produce basis vectors that minimize the reconstruction error of the convex algorithm develops locally linear models of the input, while the conic algorithm discovers features. Both algorithms are used to model handwritten digits and compared with vector quantization and principal component analysis. The neural network implementations involve feedback connections that project a reconstruction back to the input layer.
AB - Unsupervised learning algorithms based on convex and conic encoders are proposed. The encoders find the closest convex or conic combination of basis vectors to the input. The learning algorithms produce basis vectors that minimize the reconstruction error of the convex algorithm develops locally linear models of the input, while the conic algorithm discovers features. Both algorithms are used to model handwritten digits and compared with vector quantization and principal component analysis. The neural network implementations involve feedback connections that project a reconstruction back to the input layer.
UR - http://www.scopus.com/inward/record.url?scp=84899021859&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84899021859&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84899021859
SN - 0262100657
SN - 9780262100656
T3 - Advances in Neural Information Processing Systems
SP - 515
EP - 521
BT - Advances in Neural Information Processing Systems 9 - Proceedings of the 1996 Conference, NIPS 1996
PB - Neural information processing systems foundation
T2 - 10th Annual Conference on Neural Information Processing Systems, NIPS 1996
Y2 - 2 December 1996 through 5 December 1996
ER -