Add to ORKGIn many situations, a solution to a practical problem is sparse, i.e., corresponds to the case when most of the parameters describing the solution are zeros, and only a few attain non-zero values. This surprising empirical phenomenon helps solve the corresponding problems -- but it remains unclear why this phenomenon happens. In this paper, we provide a possible theoretical explanation for this mysterious phenomenon
The real-world data nowadays is usually in high dimension. For example, one data image can be repres...
International audienceWe propose an algorithm for finding sparse Nash equilibria. The algorithm is f...
University of Minnesota Ph.D. dissertation. October 2012. Major:Electrical Engineering. Advisor: Pro...
Commitment to sparseness amounts to the idea that there is an objective, worldly privileging of cert...
In many practical applications, it turned out to be efficient to assume that the signal or an image ...
Data analysis is only interesting when the data has structure — there’s not much you can do with ran...
In the last decade, the demand for statistical and computation methods for data analysis that involv...
Arguably one of the most notable forms of the principle of parsimony was formulated by the philosoph...
An overview is given of the role of the sparseness constraint in signal processing problems. It is s...
Abstract Recent years witnessed the proliferation of the notion of sparsity and its applications in ...
Analysis sparsity is a common prior in inverse problem or machine learning including special cases s...
A sparse matrix is a matrix with very few nonzero elements. Many applications in diverse fields gi...
Commitment to sparseness amounts to the idea that there is an objective, worldly privileging of cert...
It is a well known experience that for sparse structures one can find fast algorithm for some proble...
We formulate the sparse classification problem of n samples with p features as a binary convex optim...
The real-world data nowadays is usually in high dimension. For example, one data image can be repres...
International audienceWe propose an algorithm for finding sparse Nash equilibria. The algorithm is f...
University of Minnesota Ph.D. dissertation. October 2012. Major:Electrical Engineering. Advisor: Pro...
Commitment to sparseness amounts to the idea that there is an objective, worldly privileging of cert...
In many practical applications, it turned out to be efficient to assume that the signal or an image ...
Data analysis is only interesting when the data has structure — there’s not much you can do with ran...
In the last decade, the demand for statistical and computation methods for data analysis that involv...
Arguably one of the most notable forms of the principle of parsimony was formulated by the philosoph...
An overview is given of the role of the sparseness constraint in signal processing problems. It is s...
Abstract Recent years witnessed the proliferation of the notion of sparsity and its applications in ...
Analysis sparsity is a common prior in inverse problem or machine learning including special cases s...
A sparse matrix is a matrix with very few nonzero elements. Many applications in diverse fields gi...
Commitment to sparseness amounts to the idea that there is an objective, worldly privileging of cert...
It is a well known experience that for sparse structures one can find fast algorithm for some proble...
We formulate the sparse classification problem of n samples with p features as a binary convex optim...
The real-world data nowadays is usually in high dimension. For example, one data image can be repres...
International audienceWe propose an algorithm for finding sparse Nash equilibria. The algorithm is f...
University of Minnesota Ph.D. dissertation. October 2012. Major:Electrical Engineering. Advisor: Pro...