Abstract | ||
---|---|---|
KNN is one of the most popular classification methods, but it often fails to
work well with inappropriate choice of distance metric or due to the presence
of numerous class-irrelevant features. Linear feature transformation methods
have been widely applied to extract class-relevant information to improve kNN
classification, which is very limited in many applications. Kernels have been
used to learn powerful non-linear feature transformations, but these methods
fail to scale to large datasets. In this paper, we present a scalable
non-linear feature mapping method based on a deep neural network pretrained
with restricted boltzmann machines for improving kNN classification in a
large-margin framework, which we call DNet-kNN. DNet-kNN can be used for both
classification and for supervised dimensionality reduction. The experimental
results on two benchmark handwritten digit datasets show that DNet-kNN has much
better performance than large-margin kNN using a linear mapping and kNN based
on a deep autoencoder pretrained with retricted boltzmann machines. |
Year | Venue | Keywords |
---|---|---|
2009 | Clinical Orthopaedics and Related Research | neural network,artificial intelligent,boltzmann machine,distance metric |
Field | DocType | Volume |
Boltzmann machine,Dimensionality reduction,Autoencoder,Pattern recognition,Computer science,Metric (mathematics),Artificial intelligence,Encoder,Linear map,Artificial neural network,Machine learning,Scalability | Journal | abs/0906.1 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Renqiang Min | 1 | 149 | 17.61 |
David A. Stanley | 2 | 28 | 1.56 |
Zineng Yuan | 3 | 48 | 2.44 |
Anthony J. Bonner | 4 | 733 | 422.63 |
Zhaolei Zhang | 5 | 237 | 19.25 |