Whereas the variable selection has been extensively studied in the context of supervised learning, the unsupervised variable selection has attracted attention of researchers more recently as the available amount of unlabeled data has exploded. Many unsupervised variable ranking criteria were proposed and their relevance is usually demonstrated using either external cluster validity indexes or t...

BACKGROUND The increasing availability of clinical data from electronic medical records (EMRs) has created opportunities for secondary uses of health information. When used in machine learning classification, many data features must first be transformed by discretization. OBJECTIVE To evaluate six discretization strategies, both supervised and unsupervised, using EMR data. MATERIALS AND MET...

In supervised learning scenarios, feature selection has been studied widely in the literature. Selecting features in unsupervised learning scenarios is a much harder problem, due to the absence of class labels that would guide the search for relevant information. And, almost all of previous unsupervised feature selection methods are “wrapper” techniques that require a learning algorithm to eval...

A verb paradigm is a set of inflectional categories for a single verb lemma. To obtain verb paradigms we extracted left and right bigrams for the 400 most frequent verbs from over 100 million words of text, calculated the Kullback Leibler distance for each pair of verbs for left and right contexts separately, and ran a hierarchical clustering algorithm for each context. Our new method for findi...

seismic facies analysis (sfa) aims to classify similar seismic traces based on amplitude, phase,frequency, and other seismic attributes. sfa has proven useful in interpreting seismic data, allowingsignificant information on subsurface geological structures to be extracted. while facies analysis hasbeen widely investigated through unsupervised-classification-based studies, there are few casesass...

One objective for classifying textures in natural images is to achieve the best performance possible. Unsupervised techniques are suitable when no prior knowledge about the image content is available. The main drawback of unsupervised approaches is its worst performance as compared against supervised ones. We propose a new unsupervised hybrid approach based on two welltested classifiers: Vector...

It has been recently shown that a convolutional neural network can learn optical flow estimation with unsupervised learning. However, the performance of the unsupervised methods still has a relatively large gap compared to its supervised counterpart. Occlusion and large motion are some of the major factors that limit the current unsupervised learning of optical flow methods. In this work we int...

1. Introduction From a traditional point of view, knowledge exploration can be categorized into supervised learning and unsupervised learning (Jordan and Jacobs 1994). In the last decade, there have been research activities on supervised learning approaches and techniques, whereby class information is available before any knowledge exploration takes place. The most utilized approach is to achie...

With microarray gene-expression data, we compare supervised feature extraction methods with the unsupervised feature extraction methods. From experimental results, it is shown that the supervised feature extraction methods are more powerful than the unsupervised feature extraction methods in terms of class separability.

We show how a deep denoising autoencoder with lateral connections can be used as an auxiliary unsupervised learning task to support supervised learning. The proposed model is trained to minimize simultaneously the sum of supervised and unsupervised cost functions by back-propagation, avoiding the need for layerwise pretraining. It improves the state of the art significantly in the permutationin...