Babajide O. Ayinde

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In this paper, we demonstrate new techniques for data representation in the context of deep learning using agglomerative clustering. The results from previous work show that a good number of encoding and decoding filters of layered autoencoders are duplicative thereby enforcing two or more processing filters to extract the same features due to filtering(More)
This paper proposes new techniques for data representation in the context of deep learning using agglomerative clustering. Existing autoencoder-based data representation techniques tend to produce a number of encoding and decoding receptive fields of layered autoencoders that are duplicative, thereby leading to extraction of similar features, thus resulting(More)
Unsupervised feature extractors are known to perform an efficient and discriminative representation of data. Insight into the mappings they perform and human ability to understand them, however, remain very limited. This is especially prominent when multilayer deep learning architectures are used. This paper demonstrates how to remove these bottlenecks(More)
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