Efficient Dictionary Learning with Sparseness-Enforcing Projections

Efficient Sparseness-Enforcing Projections

We propose a linear time and constant space algorithm for computing Euclidean projections onto sets on which a normalized sparseness measure attains a constant value. These non-convex target sets can be characterized as intersections of a simplex and a hypersphere. Some previous methods required the vector to be projected to be sorted, resulting in at least quasilinear time complexity and linea...

SLEP: Sparse Learning with Efficient Projections

Image De-Quantizing via Enforcing Sparseness in Overcomplete Representations

We describe a method for removing quantization artifacts (de-quantizing) in the image domain, by enforcing a high degree of sparseness in its representation with an overcomplete oriented pyramid. For this purpose we devise a linear operator that returns the minimum L2-norm image preserving a set of significant coefficients, and estimate the original by minimizing the cardinality of that subset,...

Learning Dependent Dictionary Representation with Efficient Multiplicative Gaussian Process

In dictionary learning for analysis of images, spatial correlation from extracted patches can be leveraged to improve characterization power. We propose a Bayesian framework for dictionary learning, where spatial location dependencies are captured by imposing a multiplicative Gaussian process prior on the latent units representing binary activations. Data augmentation and Kronecker methods allo...

Efficient Machine Learning Using Random Projections

As an alternative to cumbersome nonlinear schemes for dimensionality reduction, the technique of random linear projection has recently emerged as a viable alternative for storage and rudimentary processing of high-dimensional data. We invoke new theory to motivate the following claim: the random projection method may be used in conjunction with standard algorithms for a multitude of machine lea...