A variety of representation learning approaches have been investigated for reinforcement learning; much less attention, however, has been given to investigating the utility of sparse coding. Outside of reinforcement learning, sparse coding representations have been widely used, with non-convex objectives that result in discriminative representations. In this work, we develop a supervised sparse...

The success of sparse representations in image modeling and recovery has motivated its use in computer vision applications. Object recognition has been effectively performed by aggregating sparse codes of local features in an image at multiple spatial scales. Though sparse coding guarantees a highfidelity representation, it does not exploit the dependence between the local features. By incorpor...

Sparse coding, a method of explaining sensory data with as few dictionary bases as possible, has attracted much attention in computer vision. For visual object category recognition, `1 regularized sparse coding is combined with the spatial pyramid representation to obtain state-of-the-art performance. However, because of its iterative optimization, applying sparse coding onto every local featur...

Recently, the sparse coding based codebook learning and local feature encoding have been widely used for image classification. The sparse coding model actually assumes the reconstruction error follows Gaussian or Laplacian distribution, which may not be accurate enough. Besides, the ignorance of spatial information during local feature encoding process also hinders the final image classificatio...

Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learn the ranking scores from data points plays an important role. Up to new, these two methods have always been used individually, assuming that data coding and ranking are two independent and irrelev...

Sparse coding is a class of unsupervised methods for learning sparse representation the input data in form linear combination dictionary and code. This framework has led to state-of-the-art results various signal processing tasks. However, classical learn code based on alternating optimizations, usually without theoretical guarantees either optimality or convergence due non-convexity problem. R...