Title
Large-Margin kNN Classification Using a Deep Encoder Network
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 Min114917.61
David A. Stanley2281.56
Zineng Yuan3482.44
Anthony J. Bonner4733422.63
Zhaolei Zhang523719.25