Before revision:  However, in a Big Data setting computing the dominant singular vectors is often restrictive due to the main memory requirements imposed by the dataset . Recently introduced randomized projection schemes attempt to mitigate this memory load by constructing approximate projections of the true dataset in a streaming setting. However, these projection methods come at the cost of approximation errors in both top singular values and vectors .
After revision:  However, in a Big Data setting computing the dominant singular vectors is often restrictive due to the main memory requirements imposed by the dataset . Recently introduced streaming Randomized SVD schemes work under the restrictive assumption that the singular value spectrum of the data has exponential decay. This is seldom true for any practical data. Although these methods are claimed to be applicable to scientific computations due to associated tail-energy error bounds, the approximation errors in both top singular values and vectors .