Multiscale Mining of fMRI Data with Hierarchical Structured Sparsity

Mining Structured Data Mining Structured Data

Learning Hierarchical and Topographic Dictionaries with Structured Sparsity

Recent work in signal processing and statistics have focused on defining new regularization functions, which not only induce sparsity of the solution, but also take into account the structure of the problem. We present in this paper a class of convex penalties introduced in the machine learning community, which take the form of a sum of l2and l∞-norms over groups of variables. They extend the c...

Representative Selection with Structured Sparsity

We propose a novel formulation to find representatives in data samples via learning with structured sparsity. To find representatives with both diversity and representativeness, we formulate the problem as a structurally-regularized learning where the objective function consists of a reconstruction error and three structured regularizers: (1) group sparsity regularizer, (2) diversity regularize...

Logistic Regression with Structured Sparsity

Binary logistic regression with a sparsity constraint on the solution plays a vital role in many high dimensional machine learning applications. In some cases, the features can be grouped together, so that entire subsets of features can be selected or zeroed out. In many applications, however, this can be very restrictive. In this paper, we are interested in a less restrictive form of structure...

Adaptive Sensing with Structured Sparsity

Adaptive sensing strategies have been proven to outperform traditional (non adaptive) compressed sensing, in terms of the signal to noise ratios that can be handled, and/or the number of measurements needed to accurately recover a signal of interest. Most existing adaptive sensing schemes for sparse signals, while work well in practice, do not take into account potential structure present in th...