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Unsupervised learning

Known as: Unsupervised approach, Unsupervised classification 
Unsupervised learning is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Since the examples given… 
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Related topics

Related topics

49 relations
Anomaly detectionArtificial intelligence for video surveillanceAutoencoderAutomatic target recognition

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2018
Highly Cited
2018
Identifying topological order through unsupervised machine learning
The Landau description of phase transitions relies on the identification of a local order parameter that indicates the onset of a… 
Highly Cited
2017
Highly Cited
2017
Occlusion Aware Unsupervised Learning of Optical Flow
It has been recently shown that a convolutional neural network can learn optical flow estimation with unsupervised learning… 
Highly Cited
2017
Highly Cited
2017
Unsupervised Video Summarization with Adversarial LSTM Networks
This paper addresses the problem of unsupervised video summarization, formulated as selecting a sparse subset of video frames… 
Highly Cited
2016
Highly Cited
2016
Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks
In this paper, we propose a new unsupervised deep learning approach called DeepBit to learn compact binary descriptor for… 
Highly Cited
2015
Highly Cited
2015
Learning Discriminative Reconstructions for Unsupervised Outlier Removal
We study the problem of automatically removing outliers from noisy data, with application for removing outlier images from an… 
Highly Cited
2012
Highly Cited
2012
Unsupervised Feature Learning for RGB-D Based Object Recognition
Recently introduced RGB-D cameras are capable of providing high quality synchronized videos of both color and depth. With its… 
Highly Cited
2011
Highly Cited
2011
Domain adaptation for object recognition: An unsupervised approach
Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that… 
Highly Cited
2009
Highly Cited
2009
Unsupervised spoken keyword spotting via segmental DTW on Gaussian posteriorgrams
In this paper, we present an unsupervised learning framework to address the problem of detecting spoken keywords. Without any… 
Review
2008
Review
2008
Unsupervised Learning and Clustering
Unsupervised learning is very important in the processing of multimedia content as clustering or partitioning of data in the… 
Highly Cited
2007
Highly Cited
2007
Unsupervised models for morpheme segmentation and morphology learning
We present a model family called Morfessor for the unsupervised induction of a simple morphology from raw text data. The model is… 
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