The present invention concerns a method of sampling a test signal. The method comprises: acquiring (21) training signals sampled at a plurality of sampling locations; running (23) an optimization procedure for determining an index set of n indices, representing a subset of the sampling locations, that maximize a function, over the training signals, of a quality parameter representing how well a given training signal is represented by the n indices; and sampling (25) the test signal at the sampling locations represented by the n indices.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of sampling a test signal, the method comprising: acquiring training signals sampled at a plurality of sampling locations; running an optimization procedure for determining an index set of n indices, representing a subset of the sampling locations, that maximize a function, over at least some of the training signals, of a quality parameter representing how well a given training signal is represented by the n indices; and sampling the test signal at the sampling locations represented by the n indices. 2. The method according to claim 1, further comprising obtaining an estimate of the test signal based only on samples obtained at the sampling locations represented by the n indices. 3. The method according to claim 1, wherein the optimization procedure forms training signal estimates for at least some of the training signals, based only on samples obtained at sampling locations represented by a candidate index set to obtain the index set. 4. The method according to claim 1, wherein the optimization procedure maximizes an average of the quality parameters of the training signals. 5. The method according to claim 1, wherein the optimization procedure maximizes a minimum of the quality parameters of the training signals. 6. The method according to claim 1, wherein the quality parameter corresponds to a given training signal energy. 7. The method according to claim 1, wherein the quality parameter is derived from a mean-square error of the training signal estimates. 8. The method according to claim 1, wherein the optimization procedure defines an optimization problem for determining the index set, and wherein the optimization problem is solved by determining modularity and/or submodularity structures in the optimization problem. 9. The method according to claim 1, wherein the optimization procedure defines an optimization problem of the type: Ω ^ =   1 m  ∑ j = 1 m  ∑ i ∈ Ω    〈 ψ i , x j 〉  q , where, q>0, m is the number of training signals x, ⊂ {Ω:|Ω|=n}, ψi is the i-th row of Ψ, which is an orthonormal basis matrix such that Ψ ∈ Cp×p, where p is a dimension of the test signal. 10. The method according to claim 1, wherein the optimization procedure defines an optimization problem of the type: Ω ^ = arg   max Ω :  Ω  = n   min j = 1 , …  ,  m  ∑ i ∈ Ω    〈 ψ i , x j 〉  q , where, q>0, m is the number of training signals x, ψi is the i-th row of Ψ, which is an orthonormal basis matrix such that Ψ ∈ Cp×p, where p is a dimension of the test signal. 11. The method according to claim 1, further comprising applying one or more weighing factors in the optimization procedure to prioritize some training signals and/or some parts of the training signals. 12. The method according to claim 1, wherein sampling of the test signal is done non-uniformly. 13. The method according to claim 1, wherein substantially no sampling of the test signal is done outside the locations given by the n indices. 14. The method according to claim 1, wherein the data sample set comprises data samples from fully sampled training signals. 15. The method according to claim 1, wherein samples of the one or more training signals have the most energy substantially at the same locations as samples of the test signal. 16. The method according to claim 1, wherein the index set is used for sampling further test signals. 17. The method according to claim 1, wherein in the determination of the index set n, one or more constraints relating to at least one of the following are taken into account: sampling apparatus, settings of the sampling apparatus, test signal type and user. 18. The method according to claim 1, wherein in the determination of the index set n, one or more constraints are imposed in the optimization procedure. 19. A signal sampling apparatus for sampling a test signal, the apparatus comprising means for: acquiring training signals sampled at a plurality of sampling locations; running an optimization procedure for determining an index set of n indices, representing a subset of the sampling locations, that maximize a function, over at least some of the training signals, of a quality parameter representing how well a given training signal is represented by the n indices; and sampling the test signal at the sampling locations represented by the n indices. 20. The signal sampling apparatus according to claim 19, wherein the signal sampling apparatus is a linear signal sampling apparatus.