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Unsupervised Feature Selection Using Iterative Shrinking and Expansion Algorithm

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2022年第6期6卷 1453-1462页

作者： Bhadra, Tapas Maulik, Ujjwal Aliah Univ Dept Comp Sci & Engn Kolkata 700160 West Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 West Bengal India

In this article, we propose an unsupervised feature selection algorithm based on the recently developed shrinking and expansion algorithm (SEA). The SEA is already an established, efficient algorithm to identify dense subgraphs within a weighted graph. To conduct SEA on a weighted graph, the dataset is first mapped onto an equivalent graph notation where each vertex corresponds to a feature, and the weight of each edge represents the normalized mutual information between the associated two features that form the edge. As a result, the feature selection problem is presented as the best way to obtain several dense sub-feature spaces by using the SEA on a weighted feature graph. The proposed feature selection algorithm consists of a two-stage structure, in which the first stage is utilized to find a number of dense feature subgraphs, whereas the second stage is used to identify a representative feature from each of these feature subgraphs. The main advantage of the proposed algorithm is that the users are not required to provide the number of features to be selected, which is a major flaw in most of the existing algorithms. The current findings from the researchers' method are better than the other state-of-the-art algorithms because the feature selection process identifies approximately one-fifth to one-third of the number of original features while providing a high accuracy score. The superiority of the proposed algorithm over the other conventional methods of feature selection is established for a number of real-life datasets. In addition, the performance of the proposed feature selection algorithm is also found to be better than other algorithms in one of the innovative application areas of computational intelligence such as ambient intelligence.

关键词： Feature extraction Mutual information Task analysis Computational intelligence approximation algorithms Probability distribution Laplace equations Pattern recognition unsupervised feature selection mutual information normalized mutual information

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3-D Path Planning Using Improved RRT* Algorithm for Robot-Assisted Flexible Needle Insertion in Multilayer Tissues

IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING

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IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING 2022年第1期45卷 50-62页

作者： Zhang, Yongkang Ju, Zhiyang Zhang, Hui Qi, Zhi Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China

In the field of minimally invasive surgery, flexible needles can avoid blood vessels and organs more flexibly compared to rigid needles. One of the main challenges when using flexible needles to reach lesions is planning a suitable path. Due to the non-holonomic characteristic of the flexible needle dynamics and the tissue deformation caused by the needle tip during the insertion, the accessibility and safety of the needle's states need to be considered in the path planning stage. In this article, we propose an adaptable algorithm by improving the canonical rapidly exploring random trees* (RRT*) algorithm to compute a path for the flexible needle to reach targets in a layered tissue environment. The improved RRT* algorithm that addresses the motion constraints of the flexible needle renders the computed path comparatively smoother and optimal in some approximation sense. In the proposed algorithm, a strategy of adapting some of its parameters for different tissues during the insertion is developed, which improves the safety of surgeries. Moreover, the path cost used in the algorithm takes the potential fields of surrounding obstacles into account, which is used to deal with the influence of the local movement of tissues during the needle puncture process. Simulations are conducted to verify the effectiveness of the proposed algorithm. The results show that the improved RRT* algorithm generates a smooth and safe path which satisfies the motion constraints of the flexible needle in layered tissue environment.

关键词： Needles Path planning Planning approximation algorithms Nonhomogeneous media Kinematics Inverse problems Flexible needle insertion multilayer tissues path planning rapidly exploring random trees* (RRT*)

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FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第8期34卷 3779-3785页

作者： Wu, Renjie Keogh, Eamonn J. Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA

Many time series data mining problems can be solved with repeated use of distance measure. Examples of such tasks include similarity search, clustering, classification, anomaly detection and segmentation. For over two decades it has been known that the Dynamic Time Warping (DTW) distance measure is the best measure to use for most tasks, in most domains. Because the classic DTW algorithm has quadratic time complexity, many ideas have been introduced to reduce its amortized time, or to quickly approximate it. One of the most cited approximate approaches is FastDTW. The FastDTW algorithm has well over a thousand citations and has been explicitly used in several hundred research efforts. In this work, we make a surprising claim. In any realistic data mining application, the approximate FastDTW is much slower than the exact DTW. This fact clearly has implications for the community that uses this algorithm: allowing it to address much larger datasets, get exact results, and do so in less time.

关键词： Time series analysis approximation algorithms Time measurement Task analysis Classification algorithms Data mining Heart beat Dynamic time warping time series analysis similarity measures data mining

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An improved algorithm for the Steiner tree problem with bounded edge-length

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2022年 123卷 20-36页

作者： Chen, Chi-Yeh Hsieh, Sun-Yuan Natl Cheng Kung Univ Dept Comp Sci & Informat Engn 1 Univ Rd Tainan Taiwan

This work firstly studies the Steiner tree problem with bounded edge-length din which dis the ratio of the maximum edge cost to the minimum edge cost. This work analyzes the algorithm of Byrka et al. [19] and shows that the approximation ratio of dln4d/d+ln4-1 + epsilon for general graphs and approximation ratio of 73.d/60.d+13 + epsilon for quasi-bipartite graphs. The algorithm implies approximation ratio of 1.162 + epsilon for the problem on complete graphs with edge distances 1 and 2. This finding represents an improvement upon the previous best approximation ratio of 1.25. This work then presents a combinatorial two-phase heuristic for the general Steiner tree in greedy strategy that achieves an approximation ratio of 1.4295. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Steiner trees approximation algorithms Network design Randomized algorithms Greedy algorithms

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Static Output Feedback Control for T-S Fuzzy Systems via a Successive Convex Optimization Algorithm

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第10期30卷 4298-4309页

作者： Ren, Yingying Ding, Da-Wei Li, Qing Xie, Xiangpeng Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Grad Sch Foshan 528399 Peoples R China Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China Nanjing Univ Post & Telecommun Inst Adv Technol Nanjing 210023 Peoples R China Chengdu Univ Sch Informat Sci & Engn Chengdu 610106 Peoples R China

This article focuses on developing a static output feedback (SOF) control scheme for Takagi-Sugeno fuzzy systems. The proposed SOF controller does not share the same premise membership functions with the model, which permits enhancing the flexibility in controller design and implementation. By contrast with the state feedback case, the SOF control generally results in nonconvex design conditions. To circumvent this problem, we develop a successive convex optimization algorithm, which is based on solving a sequence of more tractable convex optimization problems obtained by approximating the nonconvex constraints with some convex ones. As a heuristic algorithm, the validity of the developed successive convex optimization algorithm is highly affected by initial conditions, and, recognizing this, we put forward an iterative procedure for determining the feasible initial condition. Finally, two illustrative examples are presented to validate the efficiency of the proposed algorithms.

关键词： Heuristic algorithms Fuzzy systems Convex functions approximation algorithms Output feedback Optimization Closed loop systems Bilinear matrix inequality (BMI) static output feedback (SOF) successive convex optimization (SCO) algorithm Takagi-Sugeno (T-S) fuzzy systems

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Extension and its price for the connected vertex cover problem

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THEORETICAL COMPUTER SCIENCE 2022年 904卷 66-80页

作者： Ghadikolaei, Mehdi Khosravian Melissinos, Nikolaos Monnot, Jerome Pagourtzis, Aris Univ Paris 09 PSL Univ LAMSADE CNRS F-75016 Paris France Natl Tech Univ Athens Sch Elect & Comp Engn Athens 15780 Greece

We consider extension variants of VERTEX COVER and INDEPENDENT SET, following a line of research initiated in [10]. In particular, we study the Exr-CVC and the Exr-NSIS problems: given a graph G = (V, E) and a vertex set U subset of V, does there exist a minimal connected vertex cover (respectively, a maximal non-separating independent set) S, such that U subset of S (respectively, U superset of S). We present hardness results for both problems, for certain graph classes such as bipartite, chordal and weakly chordal. To this end we exploit the relation of Exr-CVC to Exr-VC, that is, to the extension variant of VERTEX COVER. We also study the Price of Extension (PoE), a measure that reflects the distance of a vertex set U to its maximum efficiently computable subset that is extensible to a minimal connected vertex cover, and provide negative and positive results for PoE in general and special graphs. (C) 2021 Published by Elsevier B.V.

关键词： Extension problems Connected vertex cover Upper connected vertex cover Price of extension Special graph classes approximation algorithms NP-completeness

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Generalized Second-Order Value Iteration in Markov Decision Processes

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第8期67卷 4241-4247页

作者： Kamanchi, Chandramouli Diddigi, Raghuram Bharadwaj Bhatnagar, Shalabh Indian Inst Sci Dept Comp Sci & Automat Bengaluru 560012 India

Value iteration is a fixed point iteration technique utilized to obtain the optimal value function and policy in a discounted reward Markov decision process (MDP). Here, a contraction operator is constructed and applied repeatedly to arrive at the optimal solution. Value iteration is a first-order method and, therefore, it may take a large number of iterations to converge to the optimal solution. Successive relaxation is a popular technique that can be applied to solve a fixed point equation. It has been shown in the literature that under a special structure of the MDP, successive overrelaxation technique computes the optimal value function faster than standard value iteration. In this article, we propose a second-order value iteration procedure that is obtained by applying the Newton-Raphson method to the successive relaxation value iteration scheme. We prove the global convergence of our algorithm to the optimal solution asymptotically and show the second-order convergence. Through experiments, we demonstrate the effectiveness of our proposed approach.

关键词： Mathematical model Convergence Newton method Standards approximation algorithms Markov processes Entropy Markov decision process value iteration successive relaxation

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Dynamic Channel Selection and Transmission Scheduling for Cognitive Radio Networks

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IEEE INTERNET OF THINGS JOURNAL 2022年第23期9卷 24429-24443页

作者： Zhu, Xinyu Huang, Yang Wu, Qihui Zhou, Fuhui Ge, Xiaohu Liu, Yuan Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Elect Spectrum Space Minist Ind & Informat Technol Nanjing 210016 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Peoples R China

Cognitive radio networks (CRNs) are expected to be promising techniques for improving the spectrum efficiency of wireless network utility in the squeezed sub-6-GHz frequency bands. Nevertheless, frequency allocation and transmission scheduling for secondary users (SUs) in CRNs suffer from no prior knowledge of other SUs' network behaviors or the distribution of the amount of data generated at each SU. As a countermeasure, this article develops a protocol for the joint channel selection and transmission scheduling such that SUs with heterogeneous data transmission demands could be served with limited spectrum resources. Then, we formulate the dynamic optimization of the protocol as mutually embedded Markov decision processes (MDPs). To address the intractable MDPs, Q-learning-based channel selection and transmission scheduling based on reinforcement learning with basis function approximation are, respectively, proposed. It is shown that compared with various baselines, the proposed channel selection algorithm enables each SU to select the best frequency-domain channel that does not interfere with other SUs. In particular, the proposed transmission scheduling algorithm outperforms algorithms based on off-the-shelf approaches, such as Q-learning and Lyapunov optimization, in terms of both energy efficiency and long-term accumulative amount of bits at each SU.

关键词： Dynamic scheduling Resource management Optimization approximation algorithms Time-frequency analysis Data communication Sensors Basis function approximation (BFA) cognitive radio network (CRN) mutually embedded Markov decision processes (MDPs) Q-learning reinforcement learning (RL) resource allocation

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Multiway Cuts with a Choice of Representatives 49

Multiway Cuts with a Choice of Representatives

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49th International Symposium on Mathematical Foundations of Computer Science, MFCS 2024

作者： Bérczi, Kristóf Király, Tamás Szabo, Daniel P. MTA-ELTE Matroid Optimization Research Group Budapest Hungary HUN-REN–ELTE Egerváry Research Group on Combinatorial Optimization Department of Operations Research Eötvös Loránd University Budapest Hungary Department of Operations Research Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9783959773355

In the Multiway Cut problem, we are given an undirected graph with nonnegative edge weights and a subset of k terminals, and the goal is to determine a set of edges of minimum total weight whose removal disconnects each terminal from the rest. The problem is APX-hard for k ≥ 3, and an extensive line of research has concentrated on closing the gap between the best upper and lower bounds for approximability and inapproximability, respectively. In this paper, we study several generalizations of Multiway Cut where the terminals can be chosen as representatives from sets of candidates T1, . . ., Tq. In this setting, one is allowed to choose these representatives so that the minimum-weight cut separating these sets via their representatives is as small as possible. We distinguish different cases depending on (A) whether the representative of a candidate set has to be separated from the other candidate sets completely or only from the representatives, and (B) whether there is a single representative for each candidate set or the choice of representative is independent for each pair of candidate sets. For fixed q, we give approximation algorithms for each of these problems that match the best known approximation guarantee for Multiway Cut. Our technical contribution is a new extension of the CKR relaxation that preserves approximation guarantees. For general q, we show o(log q)-inapproximability for all cases where the choice of representatives may depend on the pair of candidate sets, as well as for the case where the goal is to separate a fixed node from a single representative from each candidate set. As a positive result, we give a 2-approximation algorithm for the case where we need to choose a single representative from each candidate set. This is a generalization of the (2 − 2/k)-approximation for k-Cut, and we can solve it by relating the tree case to optimization over a gammoid. © Kristóf Bérczi, Tamás Király, and Daniel P. Szabo.

关键词： approximation algorithms

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Joint Activity Detection and Channel Estimation in Cell-Free Massive MIMO Networks With Massive Connectivity

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IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第1期70卷 317-331页

作者： Guo, Mangqing Gursoy, M. Cenk SUNY Syracuse Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

Cell-free massive MIMO is one of the key technologies for future wireless communications, in which users are simultaneously and jointly served by all access points (APs). In this paper, we investigate the minimum mean square error (MMSE) estimation of effective channel coefficients in cell-free massive MIMO systems with massive connectivity. To facilitate the theoretical analysis, only single measurement vector (SMV) based MMSE estimation is considered in this paper, i.e., the MMSE estimation is performed based on the received pilot signals at each AP separately. Inspired by the decoupling principle of replica symmetric postulated MMSE estimation of sparse signal vectors with independent and identically distributed (i.i.d.) non-zero components, we develop the corresponding decoupling principle for the SMV based MMSE estimation of sparse signal vectors with independent and non-identically distributed (i.n.i.d.) non-zero components, which plays a key role in the theoretical analysis of SMV based MMSE estimation of the effective channel coefficients in cell-free massive MIMO systems with massive connectivity. Subsequently, based on the obtained decoupling principle of MMSE estimation, likelihood ratio test and the optimal fusion rule, we perform user activity detection based on the received pilot signals at only one AP, or cooperation among the entire set of APs for centralized or distributed detection. Via theoretical analysis, we show that the error probabilities of both centralized and distributed detection tend to zero when the number of APs tends to infinity while the asymptotic ratio between the number of users and pilots is kept constant. We also investigate the asymptotic behavior of oracle estimation in cell-free massive MIMO systems with massive connectivity via random matrix theory. Moreover, in order to demonstrate the potential performance loss of SMV based MMSE estimation, which does not employ the correlation between the received pilot signals at differe

关键词： Estimation Massive MIMO approximation algorithms Signal processing algorithms Fading channels Mean square error methods Channel estimation MMSE estimation cell-free massive MIMO massive connectivity complex Bayesian approximate message passing activity detection likelihood ratio test

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