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The 2014 guaranteed algorithm for the robust PCA problem (with the input matrix being = +) is an alternating minimization type algorithm. [12] The computational complexity is () where the input is the superposition of a low-rank (of rank ) and a sparse matrix of dimension and is the desired accuracy of the recovered solution, i.e., ‖ ^ ‖ where is the true low-rank component and ^ is the ...
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
The PCP theorem states that NP = PCP[O(log n), O(1)],. where PCP[r(n), q(n)] is the class of problems for which a probabilistically checkable proof of a solution can be given, such that the proof can be checked in polynomial time using r(n) bits of randomness and by reading q(n) bits of the proof, correct proofs are always accepted, and incorrect proofs are rejected with probability at least 1/2.
Current and former Verizon customers who purchased postpaid wireless or data service plans and were charged an "Administrative Charge" and/or an "Administrative and Telco Recovery charge" between ...
Sydney Sweeney stunned in a series of bold, braless photos on Instagram from a rooftop in New York City, USA.Her post came just days after facing backlash for paparazzi shots of her by a pool ...
(The Center Square) – A new report from California’s Legislative Analyst’s Office shows that the young adult mortality rate has remained much higher than pre-pandemic levels. “Overall ...
We can calculate the upper and lower confidence limits of the intervals from the observed data. Suppose a dataset x 1, . . . , x n is given, modeled as realization of random variables X 1, . . . , X n. Let θ be the parameter of interest, and γ a number between 0 and 1. If there exist sample statistics L n = g(X 1, . . . , X n) and U n = h(X 1
This algorithm can easily be adapted to compute the variance of a finite population: simply divide by n instead of n − 1 on the last line.. Because SumSq and (Sum×Sum)/n can be very similar numbers, cancellation can lead to the precision of the result to be much less than the inherent precision of the floating-point arithmetic used to perform the computation.