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[1511.06335] Unsupervised Deep Embedding for Clustering Analysis
Source: arxiv.org
Article summary:
1. The paper proposes a method called Deep Embedded Clustering (DEC) which simultaneously learns feature representations and cluster assignments using deep neural networks.
2. DEC learns a mapping from the data space to a lower-dimensional feature space in which it iteratively optimizes a clustering objective.
3. Experimental evaluations on image and text corpora show significant improvement over state-of-the-art methods.
Article analysis:
The article is generally trustworthy and reliable, as it provides evidence for its claims through experimental evaluations on image and text corpora. The article does not appear to be biased or one-sided, as it presents both sides of the argument equally. It also does not appear to contain any promotional content or partiality towards any particular viewpoint. The article does not appear to be missing any points of consideration or evidence for its claims, nor does it contain any unsupported claims or unexplored counterarguments. Furthermore, the article does note possible risks associated with the proposed method, such as overfitting and poor generalization performance due to limited training data. Therefore, overall the article appears to be trustworthy and reliable in terms of its content and presentation of information.