TY - GEN
T1 - Features as sufficient statistics
AU - Geiger, D.
AU - Rudra, A.
AU - Maloney, L.
PY - 1998
Y1 - 1998
N2 - An image is often represented by a set of detected features. We get an enormous compression by representing images in this way. Furthermore, we get a representation which is little affected by small amounts of noise in the image. However, features are typically chosen in an ad hoc manner. We show how a good set of features can be obtained using sufficient statistics. The idea of sparse data representation naturally arises. We treat the 1-dimensional and 2-dimensional signal reconstruction problem to make our ideas concrete.
AB - An image is often represented by a set of detected features. We get an enormous compression by representing images in this way. Furthermore, we get a representation which is little affected by small amounts of noise in the image. However, features are typically chosen in an ad hoc manner. We show how a good set of features can be obtained using sufficient statistics. The idea of sparse data representation naturally arises. We treat the 1-dimensional and 2-dimensional signal reconstruction problem to make our ideas concrete.
UR - http://www.scopus.com/inward/record.url?scp=84890477319&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890477319&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84890477319
SN - 0262100762
SN - 9780262100762
T3 - Advances in Neural Information Processing Systems
SP - 794
EP - 800
BT - Advances in Neural Information Processing Systems 10 - Proceedings of the 1997 Conference, NIPS 1997
PB - Neural information processing systems foundation
T2 - 11th Annual Conference on Neural Information Processing Systems, NIPS 1997
Y2 - 1 December 1997 through 6 December 1997
ER -