Least-Mean-Squares Coresets for Infinite Streams

Braverman, Vladimir; Feldman, Dan; Lang, Harry G.; Rus, Daniela; Statman, Adiel

doi:10.1109/tkde.2022.3180808

Cited by 2 publications

(1 citation statement)

References 71 publications

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“…This is largely due to shortest path distance and eigenvalue calculations. This limitation could be overcome using a subset, "landmarks", of high dimensional data points to reduce the computations required for both Isomap and LLE [51][52][53][54].…”

Section: Discussionmentioning

confidence: 99%

Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning

Uwaeze,

Narayana,

Kamali

et al. 2024

Diagnostics

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Background: Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues. In addition, GBCA administration increases health care costs. Thus, reducing or eliminating GBCA administration for active lesion detection is important for improved patient safety and reduced healthcare costs. Current state-of-the-art methods for identifying active lesions in brain MRI without GBCA administration utilize data-intensive deep learning methods. Objective: To implement nonlinear dimensionality reduction (NLDR) methods, locally linear embedding (LLE) and isometric feature mapping (Isomap), which are less data-intensive, for automatically identifying active lesions on brain MRI in MS patients, without the administration of contrast agents. Materials and Methods: Fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images were included in the multiparametric MRI dataset used in this study. Subtracted pre- and post-contrast T1-weighted images were labeled by experts as active lesions (ground truth). Unsupervised methods, LLE and Isomap, were used to reconstruct multiparametric brain MR images into a single embedded image. Active lesions were identified on the embedded images and compared with ground truth lesions. The performance of NLDR methods was evaluated by calculating the Dice similarity (DS) index between the observed and identified active lesions in embedded images. Results: LLE and Isomap, were applied to 40 MS patients, achieving median DS scores of 0.74 ± 0.1 and 0.78 ± 0.09, respectively, outperforming current state-of-the-art methods. Conclusions: NLDR methods, Isomap and LLE, are viable options for the identification of active MS lesions on non-contrast images, and potentially could be used as a clinical decision tool.

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Section: Discussionmentioning

confidence: 99%

Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning

Uwaeze,

Narayana,

Kamali

et al. 2024

Diagnostics

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Streaming Algorithms with Few State Changes

Jayaram,

Woodruff,

Zhou

2024

Proc. ACM Manag. Data

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In this paper, we study streaming algorithms that minimize the number of changes made to their internal state (i.e., memory contents). While the design of streaming algorithms typically focuses on minimizing space and update time, these metrics fail to capture the asymmetric costs, inherent in modern hardware and database systems, of reading versus writing to memory. In fact, most streaming algorithms write to their memory on every update, which is undesirable when writing is significantly more expensive than reading. This raises the question of whether streaming algorithms with small space and number of memory writes are possible. We first demonstrate that, for the fundamental F p moment estimation problem with p ≥ 1, any streaming algorithm that achieves a constant factor approximation must make Ω(n 1-1/p ) internal state changes, regardless of how much space it uses. Perhaps surprisingly, we show that this lower bound can be matched by an algorithm which also has near-optimal space complexity. Specifically, we give a (1+ε)-approximation algorithm for F p moment estimation that use a near-optimal ~O ε (n 1-1/p ) number of state changes, while simultaneously achieving near-optimal space, i.e., for p∈[1,2), our algorithm uses poly(log n,1/ε) bits of space for, while for p>2, the algorithm uses ~O ε (n 1-1/p ) space. We similarly design streaming algorithms that are simultaneously near-optimal in both space complexity and the number of state changes for the heavy-hitters problem, sparse support recovery, and entropy estimation. Our results demonstrate that an optimal number of state changes can be achieved without sacrificing space complexity.

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Least-Mean-Squares Coresets for Infinite Streams

Cited by 2 publications

References 71 publications

Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning

Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning

Streaming Algorithms with Few State Changes

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