Testing Sparsity over Known and Unknown Bases

Siddharth Barman, Arnab Bhattacharyya, Suprovat Ghoshal
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:491-500, 2018.

Abstract

Sparsity is a basic property of real vectors that is exploited in a wide variety of machine learning applications. In this work, we describe property testing algorithms for sparsity that observe a low-dimensional projec- tion of the input. We consider two settings. In the first setting, we test sparsity with respect to an unknown basis: given input vectors y1,...,yp∈Rd whose concatenation as columns forms Y∈Rd×p , does Y=AX for matrices A∈Rd×m and X∈Rm×p such that each column of X is k-sparse, or is Y “far” from having such a decomposition? In the second setting, we test sparsity with respect to a known basis: for a fixed design ma- trix A∈Rd×m , given input vector y∈Rd , is y=Ax for some k-sparse vector x or is y “far” from having such a decomposition? We analyze our algorithms using tools from high-dimensional geometry and probability.

Cite this Paper

BibTeX
@InProceedings{pmlr-v80-barman18a, title = {Testing Sparsity over Known and Unknown Bases}, author = {Barman, Siddharth and Bhattacharyya, Arnab and Ghoshal, Suprovat}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {491--500}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/barman18a/barman18a.pdf}, url = {https://proceedings.mlr.press/v80/barman18a.html}, abstract = {Sparsity is a basic property of real vectors that is exploited in a wide variety of machine learning applications. In this work, we describe property testing algorithms for sparsity that observe a low-dimensional projec- tion of the input. We consider two settings. In the first setting, we test sparsity with respect to an unknown basis: given input vectors $y_1 ,...,y_p \in R^d$ whose concatenation as columns forms $Y \in R^{d \times p}$ , does $Y = AX$ for matrices $A \in R^{d\times m}$ and $X \in R^{m \times p}$ such that each column of $X$ is $k$-sparse, or is $Y$ “far” from having such a decomposition? In the second setting, we test sparsity with respect to a known basis: for a fixed design ma- trix $A \in R^{d \times m}$ , given input vector $y \in R^d$ , is $y = Ax$ for some $k$-sparse vector $x$ or is $y$ “far” from having such a decomposition? We analyze our algorithms using tools from high-dimensional geometry and probability.} }
Endnote
%0 Conference Paper %T Testing Sparsity over Known and Unknown Bases %A Siddharth Barman %A Arnab Bhattacharyya %A Suprovat Ghoshal %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-barman18a %I PMLR %P 491--500 %U https://proceedings.mlr.press/v80/barman18a.html %V 80 %X Sparsity is a basic property of real vectors that is exploited in a wide variety of machine learning applications. In this work, we describe property testing algorithms for sparsity that observe a low-dimensional projec- tion of the input. We consider two settings. In the first setting, we test sparsity with respect to an unknown basis: given input vectors $y_1 ,...,y_p \in R^d$ whose concatenation as columns forms $Y \in R^{d \times p}$ , does $Y = AX$ for matrices $A \in R^{d\times m}$ and $X \in R^{m \times p}$ such that each column of $X$ is $k$-sparse, or is $Y$ “far” from having such a decomposition? In the second setting, we test sparsity with respect to a known basis: for a fixed design ma- trix $A \in R^{d \times m}$ , given input vector $y \in R^d$ , is $y = Ax$ for some $k$-sparse vector $x$ or is $y$ “far” from having such a decomposition? We analyze our algorithms using tools from high-dimensional geometry and probability.
APA
Barman, S., Bhattacharyya, A. & Ghoshal, S.. (2018). Testing Sparsity over Known and Unknown Bases. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:491-500 Available from https://proceedings.mlr.press/v80/barman18a.html.

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