TY  - JOUR
AU  - Jaume Amores
PY  - 2015//
TI  - MILDE: multiple instance learning by discriminative embedding
T2  - KAIS
JO  - Knowledge and Information Systems
SP  - 381
EP  - 407
VL  - 42
IS  - 2
PB  - Springer London
KW  - Multi-instance learning
KW  - Codebook
KW  - Bag of words
N2  - While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data.
SN  - 0219-1377
L1  - http://refbase.cvc.uab.es/files/Amo2014.pdf
UR  - http://dx.doi.org/10.1007/s10115-013-0711-1
N1  - ADAS; 601.042; 600.057; 600.076
ID  - Jaume Amores2015
ER  -