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检索条件"主题词=Streaming algorithm"

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Spectral Guarantees for Adversarial streaming PCA 65

Spectral Guarantees for Adversarial Streaming PCA

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65th Symposium on Foundations of Computer Science

作者： Price, Eric Xun, Zhiyang Univ Texas Austin Dept Comp Sci Austin TX 78712 USA

ISBN: (纸本)9798331516758;9798331516741

In streaming PCA, we see a stream of vectors x(1),..., x(n) is an element of R-d and want to estimate the top eigenvector of their covariance matrix. This is easier if the spectral ratio R = lambda(1)/lambda(2) is large. We ask: how large does R need to be to solve streaming PCA in (O) over tilde (d) space? Existing algorithms require R = (O) over tilde (d). We show: center dot For all mergeable summaries, R = (O) over tilde(root d) is necessary. center dot In the insertion-only model, a variant of Oja's algorithm gets o(1) error for R = O(log n log d). center dot No algorithm with o(d(2)) space gets o(1) error for R = O(1). Our analysis is the first application of Oja's algorithm to adversarial streams. It is also the first algorithm for adversarial streaming PCA that is designed for a spectral, rather than Frobenius, bound on the tail;and the bound it needs is exponentially better than is possible by adapting a Frobenius guarantee.

关键词： Principal Component Analysis streaming algorithm

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Maintenance for Case Streams: A streaming Approach to Competence-Based Deletion 25th

Maintenance for Case Streams: A Streaming Approach to Compet...

引用

25th International Conference on Case-Based Reasoning (ICCBR)

作者： Zhang, Yang Zhang, Su Leake, David Indiana Univ Sch Informat & Comp Bloomington IN 47405 USA

ISBN: (纸本)9783319610306;9783319610290

The case-based reasoning community has extensively studied competence-based methods for case base compression. This work has focused on compressing a case base at a single point in time, under the assumption that the current case base provides a representative sample of cases to be seen. Large-scale streaming case sources present a new challenge for competence-based case deletion. First, in such contexts, it may be infeasible or too expensive to maintain more than a very small fraction of the overall set of cases, and the current system snapshot of the cases may not be representative of future cases, especially for domains with concept drift. Second, the interruption of processing required to compress the full case base may not be practical for large case bases in real-time streaming contexts. Consequently, such settings require maintenance methods enabling continuous incremental updates and robust to limited information. This paper presents research on addressing these problems through the use of sieve streaming, a submodular data summarization method developed for streaming data. It demonstrates how the approach enables the maintenance process to trade off between maintenance cost and competence retention and assesses its performance compared to baseline competence-based deletion methods for maintenance. Results support the benefit of the approach for large-scale streaming data.

关键词： Case-based maintenance Case deletion Sieve streaming streaming algorithm

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streaming Fair k-Center Clustering over Massive Dataset with Performance Guarantee 28th

Streaming Fair k-Center Clustering over Massive Dataset with...

引用

28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Lin, Zeyu Guo, Longkun Jia, Chaoqi Fuzhou Univ Sch Math & Stat Fuzhou 350116 Peoples R China RMIT Univ Sch Accounting Informat Syst & Supply Chain Melbourne Vic 3000 Australia Qilu Univ Technol Sch Comp Sci Shandong Acad Sci Jinan 250316 Peoples R China

ISBN: (纸本)9789819722617;9789819722594

Emerging applications are imposing challenges for incorporating fairness constraints into k-center clustering in the streaming setting. Different from the traditional k-center problem, the fairness constraints require that the input points be divided into disjoint groups and the number of centers from each group is constrained by a given upper bound. Moreover, observing the applications of fair k-center inmassive datasets, we consider the problem in the streaming setting, where the data points arrive in a streaming manner that each point can be processed at its arrival. As themain contributions, we propose a two-pass streaming algorithm for the fair k-center problem with two groups, achieving an approximation ratio of 3 + epsilon and consuming only O(k log n) memory and O(k) update time, matching the state-of-art ratio for the offline setting. Then, we show that the algorithm can be easily improved to a one-pass streaming algorithm with an approximation ratio of 7+ epsilon and the same memory complexity and update time. Moreover, we show that our algorithm can be simply tuned to solve the case with an arbitrary number of groups while achieving the same ratio and space complexity. Lastly, we carried out extensive experiments to evaluate the practical performance of our algorithm compared with the state-of-the-art algorithms.

关键词： Fair k-center clustering streaming algorithm Approximation algorithm

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First Efficient Convergence for streaming k-PCA: a Global, Gap-Free, and Near-Optimal Rate 58

First Efficient Convergence for Streaming k-PCA: a Global, G...

引用

58th IEEE Annual Symposium on Foundations of Computer Science (FOCS)

作者： Allen-Zhu, Zeyuan Li, Yuanzhi Microsoft Res Cambridge MA 02142 USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781538634646

We study streaming principal component analysis (PCA), that is to find, in Omicron(dk) space, the top k eigenvectors of a d x d hidden matrix Sigma with online vectors drawn from covariance matrix Sigma. We provide global convergence for Oja's algorithm which is popularly used in practice but lacks theoretical understanding for k > 1. We also provide a modified variant Oja(++) that runs even faster than Oja's. Our results match the information theoretic lower bound in terms of dependency on error, on eigengap, on rank k, and on dimension d, up to poly-log factors. In addition, our convergence rate can be made gap-free, that is proportional to the approximation error and independent of the eigengap. In contrast, for general rank k, before our work (1) it was open to design any algorithm with efficient global convergence rate;and (2) it was open to design any algorithm with (even local) gap-free convergence rate in Omicron(dk) space.

关键词： principal component analysis streaming algorithm online algorithm global convergence stochastic optimization convergence optimal algorithm nonconvex optimization

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Parametric streaming Two-Stage Submodular Maximization 1

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16th International Conference on Theory and Applications of Models of Computation (TAMC)

作者： Yang, Ruiqi Xu, Dachuan Guo, Longkun Zhang, Dongmei Beijing Univ Technol Dept Operat Res & Sci Comp Beijing 100124 Peoples R China Qilu Univ Technol Sch Comp Sci & Technol Shandong Acad Sci Jinan 250353 Peoples R China Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250101 Peoples R China

ISBN: (数字)9783030592677

ISBN: (纸本)9783030592660;9783030592677

We study the submodular maximization problem in generalized streaming setting using a two-stage policy. In the streaming context, elements are released in a fashion that an element is revealed at one time. Subject to a limited memory capacity, the problem aims to sieve a subset of elements with a sublinear size l, such that the expecting objective value of all utility functions over the summarized subsets has a performance guarantee. We present a generalized one pass, (gamma(5)(min)/(5 + 2 gamma(2)(min)) - O(is an element of))-approximation, which consumes O(is an element of(-1) l log(l gamma(-1)(min))) memory and runs in O(is an element of(-1) kmn log(l gamma(-1)(min))) time, where k, n, m and gamma(min) denote the cardinality constraint, the element stream size, the amount of the learned functions, and the minimum generic submodular ratio of the learned functions, respectively.

关键词： Submodular maximization streaming algorithm Submodular ratio Approximation ratio

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streaming Submodular Maximization with the Chance Constraint

Streaming Submodular Maximization with the Chance Constraint

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3rd International Joint Conference on Theoretical Computer Science (IJTCS) / 16th International Conference on Frontiers of algorithmic Wisdom (FAW)

作者： Gong, Shufang Liu, Bin Fang, Qizhi Ocean Univ China Sch Math Sci Qingdao 266100 Shandong Peoples R China

ISBN: (纸本)9783031207952;9783031207969

Submodular optimization plays a significant role in combinatorial problems due to its diminishing marginal return property. Many artificial intelligence and machine learning problems can be cast as submodular maximization problems with applications in object detection, data summarization, and video summarization. In this paper, we consider the problem of monotone submodular function maximization in the streaming setting with the chance constraint. Using mainly the idea of guessing the threshold, we propose streaming algorithms and prove good approximation guarantees and computational complexity. In our experiments, we demonstrate the efficiency of our algorithm on synthetic data for the influence maximization problem and indicate that even if the strong restriction of chance constraint is imposed, we can still get a good solution. To the best of our knowledge, this is the first paper to study the problem of monotone submodular function maximization with chance constraint in the streaming model.

关键词： Submodular maximization streaming algorithm Chance constraint

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streaming Graph Partitioning for Large Graphs with Limited Memory 15

Streaming Graph Partitioning for Large Graphs with Limited M...

引用

15th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) / 16th IEEE International Conference on Ubiquitous Computing and Communications (IUCC)

作者： Li, Qi Zhong, Jiang Zheng, Linjiang Li, Xue Chongqing Univ Coll Comp Sci Chongqing Peoples R China Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing Peoples R China Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia

ISBN: (纸本)9781538637906

With the graph data scale constantly expanding, the personal computer has brought in severe challenge for the traditional graph partitioning because of its limited memory capacity. The streaming model has been applied in graph partitioning in recent years because it is more efficient than offline partitioning. However, the cache storage structure of original streaming method is inefficient for searching operations on the one hand, on the other hand, the efficiency gradually is reduced with the number of vertices which have been allocated increased because there is no space left for storing more in memory. A caching strategy for streaming algorithm is put forward in this paper including efficient cache storage structure, which uses the vertex and its neighbors' subset information as basic entry. The cache management module manages cache content. Our method is effective on the condition whether it has limitation to cache capacity or not. By using our cache strategy, it only takes about 25 minutes for partitioning twitter-2010 that have 1.4 billion edges while the original streaming method needs 42 minutes, As can prove the effectiveness and superiority of our method.

关键词： complex networks graph partitioning streaming algorithm data cache memory computing

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Parallel Triangle Counting in Massive streaming Graphs 13

Parallel Triangle Counting in Massive Streaming Graphs

引用

22nd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Tangwongsan, Kanat Pavan, A. Tirthapura, Srikanta IBM Res Yorktown Hts NY 10598 USA Iowa State Univ Ames IA USA

ISBN: (纸本)9781450322638

The number of triangles in a graph is a fundamental metric widely used in social network analysis, link classification and recommendation, and more. In these applications, modern graphs of interest tend to both large and dynamic. This paper presents the design and implementation of a fast parallel algorithm for estimating the number of triangles in a massive undirected graph whose edges arrive as a stream. Our algorithm is designed for shared-memory multicore machines and can make efficient use of parallelism and the memory hierarchy. We provide theoretical guarantees on performance and accuracy, and our experiments on real-world datasets show accurate results and substantial speedups compared to an optimized sequential implementation.

关键词： triangle counting streaming algorithm parallel algorithm parallel cache oblivious (PCO) massive graphs

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On the streaming Indistinguishability of a Random Permutation and a Random Function 39th

On the Streaming Indistinguishability of a Random Permutatio...

引用

39th Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT)

作者： Dinur, Itai Ben Gurion Univ Negev Dept Comp Sci Beer Sheva Israel

ISBN: (纸本)9783030457242;9783030457235

An adversary with S bits of memory obtains a stream of Q elements that are uniformly drawn from the set {1, 2, ... , N}, either with or without replacement. This corresponds to sampling Q elements using either a random function or a random permutation. The adversary's goal is to distinguish between these two cases. This problem was first considered by Jaeger and Tessaro (EUROCRYPT 2019), which proved that the adversary's advantage is upper bounded by root Q . S/N. Jaeger and Tessaro used this bound as a streaming switching lemma which allowed proving that known time-memory tradeoff attacks on several modes of operation (such as counter-mode) are optimal up to a factor of O(log N) if Q . S approximate to N. However, the bound's proof assumed an unproven combinatorial conjecture. Moreover, if Q . S << N there is a gap between the upper bound of root Q . S/N and the Q . S/N advantage obtained by known attacks. In this paper, we prove a tight upper bound (up to poly-logarithmic factors) of O(log Q . Q . S/N) on the adversary's advantage in the streaming distinguishing problem. The proof does not require a conjecture and is based on a hybrid argument that gives rise to a reduction from the unique-disjointness communication complexity problem to streaming.

关键词： streaming algorithm Time-memory tradeoff Communication complexity Provable security Switching lemma Mode of operation

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Fairness in streaming Submodular Maximization Subject to a Knapsack Constraint 24

Fairness in Streaming Submodular Maximization Subject to a K...

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30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

作者： Cui, Shuang Han, Kai Tang, Shaojie Li, Feng Luo, Jun Univ Sci & Technol China Sch Comp Sci & Technol Hefei Anhui Peoples R China Soochow Univ Sch Comp Sci & Technol Suzhou Jiangsu Peoples R China Univ Texas Dallas Richardson TX USA Shandong Univ Sch Comp Sci & Technol Jinan Shandong Peoples R China Qilu Univ Technol Qingdao Key Lab Comp Power Network & Informat Secur Minist Educ Jinan Shandong Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798400704901

Submodular optimization has been identified as a powerful tool for many data mining applications, where a representative subset of moderate size needs to be extracted from a large-scale dataset. In scenarios where data points possess sensitive attributes such as age, gender, or race, it becomes imperative to integrate fairness measures into submodular optimization to mitigate bias and discrimination. In this paper, we study the fundamental problem of fair submodular maximization subject to a knapsack constraint and propose the first streaming algorithm for it with provable performance guarantees for both monotone and non-monotone submodular functions. As a byproduct, we also propose a streaming algorithm for submodular maximization subject to a partition matroid and a knapsack constraint, significantly improving the performance bounds achieved by previous work. We conduct extensive experiments on real-world applications such as movie recommendation, image summarization, and maximum coverage in social networks. The experimental results strongly demonstrate the superiority of our proposed algorithms in terms of both fairness and utility.

关键词： data stream mining fairness submodular maximization streaming algorithm

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