503
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Representation Learning: A Review and New Perspectives

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.

          Related collections

          Most cited references198

          • Record: found
          • Abstract: not found
          • Article: not found

          Gradient-based learning applied to document recognition

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A fast learning algorithm for deep belief nets.

              We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind.
                Bookmark

                Author and article information

                Journal
                IEEE Transactions on Pattern Analysis and Machine Intelligence
                IEEE Trans. Pattern Anal. Mach. Intell.
                Institute of Electrical and Electronics Engineers (IEEE)
                0162-8828
                2160-9292
                August 2013
                August 2013
                : 35
                : 8
                : 1798-1828
                Article
                10.1109/TPAMI.2013.50
                23787338
                73161184-2df8-4f89-b340-cf3ffbffc9e8
                © 2013

                https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html

                History

                Comments

                Comment on this article