87 research outputs found
Sparse Vector Distributions and Recovery from Compressed Sensing
It is well known that the performance of sparse vector recovery algorithms
from compressive measurements can depend on the distribution underlying the
non-zero elements of a sparse vector. However, the extent of these effects has
yet to be explored, and formally presented. In this paper, I empirically
investigate this dependence for seven distributions and fifteen recovery
algorithms. The two morals of this work are: 1) any judgement of the recovery
performance of one algorithm over that of another must be prefaced by the
conditions for which this is observed to be true, including sparse vector
distributions, and the criterion for exact recovery; and 2) a recovery
algorithm must be selected carefully based on what distribution one expects to
underlie the sensed sparse signal.Comment: Originally submitted to IEEE Signal Processing Letters in March 2011,
but rejected June 2011. Revised, expanded, and submitted July 2011 to EURASIP
Journal special issue on sparse signal processin
The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use
The GTZAN dataset appears in at least 100 published works, and is the
most-used public dataset for evaluation in machine listening research for music
genre recognition (MGR). Our recent work, however, shows GTZAN has several
faults (repetitions, mislabelings, and distortions), which challenge the
interpretability of any result derived using it. In this article, we disprove
the claims that all MGR systems are affected in the same ways by these faults,
and that the performances of MGR systems in GTZAN are still meaningfully
comparable since they all face the same faults. We identify and analyze the
contents of GTZAN, and provide a catalog of its faults. We review how GTZAN has
been used in MGR research, and find few indications that its faults have been
known and considered. Finally, we rigorously study the effects of its faults on
evaluating five different MGR systems. The lesson is not to banish GTZAN, but
to use it with consideration of its contents.Comment: 29 pages, 7 figures, 6 tables, 128 reference
When 'exact recovery' is exact recovery in compressed sensing simulation
In a simulation of compressed sensing (CS), one must test whether the recovered solution \vax is the true solution \vx, i.e., ``exact recovery.''Most CS simulations employ one of two criteria: 1) the recovered support is the true support; or 2) the normalized squared error is less than . We analyze these exact recovery criteria independent of any recovery algorithm, but with respect to signal distributions that are often used in CS simulations. That is, given a pair (\vax,\vx), when does ``exact recovery'' occur with respect to only one or both of these criteria for a given distribution of \vx? We show that, in a best case scenario, sets a maximum allowed missed detection ratein a majority sense
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