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JMLR
2012
11 years 7 months ago
Online Incremental Feature Learning with Denoising Autoencoders
While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, ...
Guanyu Zhou, Kihyuk Sohn, Honglak Lee
ICML
2008
IEEE
14 years 5 months ago
Extracting and composing robust features with denoising autoencoders
Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to use...
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pi...
ICCV
2011
IEEE
12 years 4 months ago
Gradient-based learning of higher-order image features
Recent work on unsupervised feature learning has shown that learning on polynomial expansions of input patches, such as on pair-wise products of pixel intensities, can improve the...
Roland Memisevic
ICPR
2008
IEEE
13 years 11 months ago
Online anomal movement detection based on unsupervised incremental learning
We propose an online anomal movement detection method using incremental unsupervised learning. As the feature for discrimination, we extract the principal component of the spatio-...
Kyoko Sudo, Tatsuya Osawa, Hidenori Tanaka, Hideki...
ICASSP
2011
IEEE
12 years 8 months ago
Denoising sparse noise via online dictionary learning
The idea of learning overcomplete dictionaries based on the paradigm of compressive sensing has found numerous applications, among which image denoising is considered one of the m...
Anoop Cherian, Suvrit Sra, Nikolaos Papanikolopoul...