4,948 research outputs found
Embedding Principal Component Analysis for Data Reductionin Structural Health Monitoring on Low-Cost IoT Gateways
Principal component analysis (PCA) is a powerful data reductionmethod for
Structural Health Monitoring. However, its computa-tional cost and data memory
footprint pose a significant challengewhen PCA has to run on limited capability
embedded platformsin low-cost IoT gateways. This paper presents a
memory-efficientparallel implementation of the streaming History PCA
algorithm.On our dataset, it achieves 10x compression factor and 59x
memoryreduction with less than 0.15 dB degradation in the
reconstructedsignal-to-noise ratio (RSNR) compared to standard PCA. More-over,
the algorithm benefits from parallelization on multiple cores,achieving a
maximum speedup of 4.8x on Samsung ARTIK 710
Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
Fair Principal Component Analysis (PCA) is a problem setting where we aim to
perform PCA while making the resulting representation fair in that the
projected distributions, conditional on the sensitive attributes, match one
another. However, existing approaches to fair PCA have two main problems:
theoretically, there has been no statistical foundation of fair PCA in terms of
learnability; practically, limited memory prevents us from using existing
approaches, as they explicitly rely on full access to the entire data. On the
theoretical side, we rigorously formulate fair PCA using a new notion called
\emph{probably approximately fair and optimal} (PAFO) learnability. On the
practical side, motivated by recent advances in streaming algorithms for
addressing memory limitation, we propose a new setting called \emph{fair
streaming PCA} along with a memory-efficient algorithm, fair noisy power method
(FNPM). We then provide its {\it statistical} guarantee in terms of
PAFO-learnability, which is the first of its kind in fair PCA literature.
Lastly, we verify the efficacy and memory efficiency of our algorithm on
real-world datasets.Comment: 42 pages, 5 figures, 4 tables. Accepted to the 37th Conference on
Neural Information Processing Systems (NeurIPS 2023
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