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Learning to Hash for Indexing Big Data—A Survey

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      Most cited references 88

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      Deep learning.

      Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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        Visualization of an Oxygen-deficient Bottom Water Circulation in Osaka Bay, Japan

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          Reducing the dimensionality of data with neural networks.

          High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such "autoencoder" networks, but this works well only if the initial weights are close to a good solution. We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.
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            Author and article information

            Journal
            Proceedings of the IEEE
            Proc. IEEE
            Institute of Electrical and Electronics Engineers (IEEE)
            0018-9219
            1558-2256
            January 2016
            January 2016
            : 104
            : 1
            : 34-57
            10.1109/JPROC.2015.2487976
            © 2016
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