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33rd Conference on Learning Theory (COLT)

作者： Holtzman, Guy Soffer, Adam Vilenchik, Dan Ben Gurion Univ Negev Beer Sheva Israel

The taxing computational effort that is involved in solving some high-dimensional statistical problems, in particular problems involving non-convex optimization, has popularized the development and analysis of algorithms that run efficiently (polynomial-time) but with no general guarantee on statistical consistency. In light of the ever-increasing compute power and decreasing costs, a more useful characterization of algorithms is by their ability to calibrate the invested computational effort with various characteristics of the input at hand and with the available computational resources. We propose a new greedy algorithm for the l(0)-sparse PCA problem which supports the calibration principle. We provide both a rigorous analysis of our algorithm in the spiked covariance model, as well as simulation results and comparison with other existing methods. Our findings show that our algorithm recovers the spike in SNR regimes where all polynomial-time algorithms fail while running in a reasonable parallel-time on a cluster.

关键词： Sparse PCA non-convex optimization anytime algorithms average case analysis

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Next Integrated Result Modelling for Stopping the Text Field Recognition Process in a Video Using a Result Model with Per-Character Alternatives 12

Next Integrated Result Modelling for Stopping the Text Field...

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12th International Conference on Machine Vision (ICMV)

作者： Bulatov, Konstantin Savelyev, Boris Arlazarov, Vladimir V. Moscow Inst Phys & Technol MIPT Moscow Russia Smart Engines Moscow Russia Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow Russia

ISBN: (纸本)9781510636446

In the field of document analysis and recognition using mobile devices for capturing, and the field of object recognition in a video stream, an important problem is determining the time when the capturing process should be stopped. Efficient stopping influences not only the total time spent for performing recognition and data entry, but the expected accuracy of the result as well. This paper is directed on extending the stopping method based on next integrated recognition result modelling, in order for it to be used within a string result recognition model with per-character alternatives. The stopping method and notes on its extension are described, and experimental evaluation is performed on an open dataset MIDV-500. The method was compares with previously published methods based on input observations clustering. The obtained results indicate that the stopping method based on the next integrated result modelling allows to achieve higher accuracy, even when compared with the best achievable configuration of the competing methods.

关键词： recognition in video stream mobile OCR stopping rules decision making mobile document recognition anytime algorithms

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Gradual discovery with closure structure of a concept lattice 15th

Gradual discovery with closure structure of a concept lattic...

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15th International Conference on Concept Lattices and Their Applications, CLA 2020

作者： Makhalova, Tatiana Kuznetsov, Sergei O. Napoli, Amedeo Universite de Lorraine CNRS Inria LORIA NancyF-54000 France National Research University Higher School of Economics Moscow Russia

An approximate discovery of closed itemsets is usually based on either setting a frequency threshold or computing a sequence of projections. Both approaches, being incremental, do not provide any estimate of the size of the next output and do not ensure that "more interesting patterns" will be generated first. We propose to generate closed itemsets incrementally, w.r.t. the size of the smallest (cardinality-minimal or minimum) generators and show that this approach (i) exhibits anytime property, and (ii) first generates itemsets of better quality and then those of lower quality. © 2020 CEUR-WS. All rights reserved.

关键词： anytime algorithms Closed itemsets Pattern Mining

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Weighted combination of per-frame recognition results for text recognition in a video stream

COMPUTER OPTICS

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COMPUTER OPTICS 2021年第1期45卷 77-89页

作者： Petrova, O. Bulatov, K. Arlazarov, V. V. Arlazarov, V. L. FRC CSC RAS Moscow Russia Smart Engines Serv LLC Moscow Russia State Univ Moscow Inst Phys & Technol Moscow Russia

The scope of uses of automated document recognition has extended and as a result, recognition techniques that do not require specialized equipment have become more relevant. Among such techniques, document recognition using mobile devices is of interest. However, it is not always possible to ensure controlled capturing conditions and, consequentially, high quality of input images. Unlike specialized scanners, mobile cameras allow using a video stream as an input, thus obtaining several images of the recognized object, captured with various characteristics. In this case, a problem of combining the information from multiple input frames arises. In this paper, we propose a weighing model for the process of combining the per-frame recognition results, two approaches to the weighted combination of the text recognition results, and two weighing criteria. The effectiveness of the proposed approaches is tested using datasets of identity documents captured with a mobile device camera in different conditions, including perspective distortion of the document image and low lighting conditions. The experimental results show that the weighting combination can improve the text recognition result quality in the video stream, and the per-character weighting method with input image focus estimation as a base criterion allows one to achieve the best results on the datasets analyzed.

关键词： mobile OCR video stream anytime algorithms weighted combination ensemble methods

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anytime RECTANGLE EXPANSION A* ALGORITHM FOR TIME-LIMITED PATHFINDING

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INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION 2019年第3期34卷 253-265页

作者： Zhang, An Li, Chong Bi, Wenhao Northwestern Polytech Univ Sch Aeronaut Xian Shaanxi Peoples R China

anytime algorithms are well suited for time-limited pathfinding problems, which find a feasible sub-optimal solution quickly and then continually work on improving it until time runs out. In this paper, a new anytime algorithm is introduced, called anytime Rectangle Expansion A* (AREA*), which is the anytime variant of basic Rectangle Expansion A* (REA*). AREA* runs an accelerated sub-optimal search and then an incomplete REA* will repair the sub-optimality. AREA* can not only return the first feasible solution much faster than existing anytime algorithms but also converge to the optimal solution in almost the same speed with REA*, which is an order of magnitude and more faster than A*. AREA* also provides narrower and more accurate sub-optimality bounds of its solutions. Experimental results for typical benchmark problem sets show that, compared with the existing anytime techniques and the basic REA*, the new algorithm shows a significant improvement in performance for time-limited pathfinding problems.

关键词： Time-limited pathfinding heuristic algorithms grid maps A* algorithm anytime algorithms

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anytime Multi-Agent Path Finding via Large Neighborhood Search 21

Anytime Multi-Agent Path Finding via Large Neighborhood Sear...

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International Conference on Autonomous Agents and Multiagent Systems

作者： Jiaoyang Li Zhe Chen Daniel Harabor Peter J. Stuckey Sven Koenig University of Southern California Monash University

ISBN: (纸本)9781713832621

Multi-Agent Path Finding (MAPF) is the challenging problem of computing collision-free paths for a cooperative team of moving agents. algorithms for solving MAPF can be categorized on a spectrum. At one end are (bounded-sub)optimal algorithms that can find high-quality solutions for small problems. At the other end are unbounded-suboptimal algorithms (including prioritized and rule-based algorithms) that can solve very large practical problems but usually find low-quality solutions. In this paper, we consider a third approach that combines both advantages: anytime algorithms that quickly find an initial solution, including for large problems, and that subsequently improve the solution to near-optimal as time progresses. To improve the solution, we replan subsets of agents using Large Neighborhood Search, a popular meta-heuristic often applied in combinatorial optimization. Empirically, we compare our algorithm MAPF-LNS to the state-of-the-art anytime MAPF algorithm anytime BCBS and report significant gains in scalability, runtime to the first solution, and speed of improving solutions.

关键词： Multi-Agent path finding Large neighborhood search anytime algorithms algorithms Solutions Neighborhood FINDINGS Byways Solution Agents

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Analyzing the Use of anytime algorithms on an Unmanned Aerial Vehicle

Analyzing the Use of Anytime Algorithms on an Unmanned Aeria...

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3rd Brazilian Symposium on Computing Systems Engineering (SBESC)

作者： Goncalves, F. S. Becker, L. B. Raffo, G. V. Univ Fed Santa Catarina Dept Automat & Syst BR-88040900 Florianopolis SC Brazil Univ Fed Minas Gerais Dept Elect Engn BR-88040900 Belo Horizonte MG Brazil

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Fast anytime retrieval with confidence in large-scale temporal case bases

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KNOWLEDGE-BASED SYSTEMS 2020年 206卷 106374-106374页

作者： Oguz Mulayim, Mehmet Lluis Arcos, Josep Spanish Natl Res Council Artificial Intelligence Res Inst IIIA CSIC Campus UAB Barcelona 08193 Spain

This work is about speeding up retrieval in Case-Based Reasoning (CBR) for large-scale case bases (CBs) comprised of temporally related cases in metric spaces. A typical example is a CB of electronic health records where consecutive sessions of a patient forms a sequence of related cases. k-Nearest Neighbors (kNN) search is a widely used algorithm in CBR retrieval. However, brute-force kNN is impossible for large CBs. As a contribution to efforts for speeding up kNN search, we introduce an anytime kNN search methodology and algorithm. anytime Lazy kNN finds exact kNNs when allowed to run to completion with remarkable gain in execution time by avoiding unnecessary neighbor assessments. For applications where the gain in exact kNN search may not suffice, it can be interrupted earlier and it returns best-so-far kNNs together with a confidence value attached to each neighbor. We describe the algorithm and methodology to construct a probabilistic model that we use both to estimate confidence upon interruption and to automatize the interruption at desired confidence thresholds. We present the results of experiments conducted with publicly available datasets. The results show superior gains compared to brute-force search. We reach to an average gain of 87.18% with 0.98 confidence and to 96.84% with 0.70 confidence. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Large-scale case-based reasoning Exact and approximate k-nearest neighbor search anytime algorithms

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On optimal stopping strategies for text recognition in a video stream as an application of a monotone sequential decision model

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INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION 2019年第3期22卷 303-314页

作者： Bulatov, Konstantin Razumnyi, Nikita Arlazarov, Vladimir V. FRC CSC RAS Inst Syst Anal Moscow Russia Smart Engines Moscow Russia Moscow Inst Phys & Technol Moscow Russia

The paper describes the problem of stopping the text field recognition process in a video stream, which is a novel problem, particularly relevant to real-time mobile document recognition systems. A decision-theoretic framework for this problem is provided, and similarities with existing stopping rule problems are explored. Following the theoretical works on monotone stopping rule problems, a strategy is proposed based on thresholding the estimation of the expected difference between consequent recognition results. The efficiency of this strategy is evaluated on an openly accessible dataset. The results show that this method outperforms the previously published methods based on identical results cluster size thresholding. Notes on future work include incorporation of recognition result confidence estimations in the proposed model and more precise evaluation of the observation cost.

关键词： Recognition in video stream Mobile OCR Stopping rules Decision making Mobile document recognition anytime algorithms

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anytime Subgroup Discovery in Numerical Domains with Guarantees

Anytime Subgroup Discovery in Numerical Domains with Guarant...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Belfodil, Aimene Belfodil, Adnene Kaytoue, Mehdi Univ Lyon CNRS INSA Lyon LIRIS UMR 5205 F-69621 Lyon France Mobile Devices Ingn 100 Ave Stalingrad F-94800 Villejuif France Infologic 99 Ave Lyon F-26500 Bourg Les Valence France

ISBN: (纸本)9783030109288;9783030109271

Subgroup discovery is the task of discovering patterns that accurately discriminate a class label from the others. Existing approaches can uncover such patterns either through an exhaustive or an approximate exploration of the pattern search space. However, an exhaustive exploration is generally unfeasible whereas approximate approaches do not provide guarantees bounding the error of the best pattern quality nor the exploration progression ("How far are we of an exhaustive search"). We design here an algorithm for mining numerical data with three key properties w.r.t. the state of the art: (i) It yields progressively interval patterns whose quality improves over time;(ii) It can be interrupted anytime and always gives a guarantee bounding the error on the top pattern quality and (iii) It always bounds a distance to the exhaustive exploration. After reporting experimentations showing the effectiveness of our method, we discuss its generalization to other kinds of patterns. Code related to this paper is available at: https://***/Adnene93/RefineAndMine.

关键词： Subgroup discovery anytime algorithms Discretization

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