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It extracts representations directly from unsupervised data without human interference.
For instance, an unsupervised data mining task (clustering) requires an accurate measure of distance or similarity.
Confounding factors in unsupervised data can lead to undesirable clustering results.
PCA is an unsupervised data transformation procedure of complex data sets.
More specifically, it aids in automatically extracting complex data representations from large volumes of unsupervised data.
Deep learning algorithms use a huge amount of unsupervised data to automatically extract complex representation.
we can use some unsupervised data in training a Deep Learning model [92, 93].
Their work clearly shows how Deep Learning methods can extract high-level features from unsupervised data and demonstrates the advantages of using Deep Learning with unsupervised data (Big Data).
Their work was a large scale investigation on the feasibility of building high-level features with Deep Learning using only unlabeled (unsupervised) data, and clearly demonstrated the benefits of using Deep Learning with unsupervised data.
The unsupervised data mining technique of self-organizing maps is used to group the participating countries into homogenous clusters.
Deep Learning is beneficial in facing a large amount of unsupervised data (Big Data) like data provided in social media.
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