ArticleProceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data2019
A Streaming model for Generalized Rayleigh with extension to Minimum Noise Fraction.
Article in Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The Rayleigh quotient optimization is the maximization of a rational function, or a max-min problem, with simultaneous maximization of the numerator function and minimization of the denominator function. Here, we describe a low-rank, streaming solution for Rayleigh quotient optimization applicable for big-data scenarios where the data matrix is too large to be fully loaded into main memory. We apply this for a maximization of the Signal to Noise ratio of big-data, of very large static and dynamic data. Our implementation is shown to achieve faster processing time compared to a standard data read into memory. We demonstrate the trade-offs with synthetic and real data, on different scales to validate the approach in terms of accuracy, speed and storage.
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