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Sparse Coding
Publications: 950| Citation Count: 11,077
Stemming Variations: sparsely coded, SPARSE CODE, sparse codes, sparse coded, sparseness code
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    • Sparse coding is an unsupervised learning algorithm that learns a succinct high-level representation of the inputs given only unlabeled data; it represents each input as a sparse linear combination of a set of basis functions. Originally applied to modeling the human visual cortex, sparse coding has also been shown to be useful for self-taught learning, in which the goal is to solve a supervised classification task given access to additional unlabeled data drawn from dierent classes than that in the supervised learning problem...

    Roger Grosseet al. Shift-Invariant Sparse Coding for Audio Classification

    • Sparse coding' is a ubiquitous strategy employed in the sensory information processing system of mammals. Some work has focused on the validation of this strategy through finding the sparse component of sensory input, and then illustrating a fact that the resulting basis functions or corresponding filter response have the visually similar receptive field to those found in primary visual cortex (V1)...

    Tan Shanet al. new evidences for sparse coding strategy employed in visual neurons: f...

    • Sparse coding is an unsupervised learning algorithm for finding concise, slightly higherlevel representations of an input, and has been successfully applied to self-taught learning (Raina et al., 2007), where the goal is to use unlabeled data to help on a supervised learning task, even if the unlabeled data cannot be associated with the labels of the supervised task. However, sparse coding uses a Gaussian noise model and a quadratic loss function, and thus performs poorly if applied to binary valued, integer valued, or other non-Gaussian data, such as text...

    Honglak Leeet al. Exponential family sparse coding with application to self-taught learn...

    • Sparse coding is an unsupervised learning algorithm for finding concise, slightly higher-level representations of inputs, and has been successfully applied to self-taught learning, where the goal is to use unlabeled data to help on a supervised learning task, even if the unlabeled data cannot be associated with the labels of the supervised task (Raina et al., 2007). However, sparse coding uses a Gaussian noise model and a quadratic loss function, and thus performs poorly if applied to binary valued, integer valued, or other non-Gaussian data, such as text...

    Honglak Leeet al. Exponential Family Sparse Coding with Applications to Self-taught Lear...

    • Sparse coding is a method for finding a representation of data in which each of the components of the representation is only rarely s ignificantly active...

    Aapo Hyvärinenet al. Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estim...

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