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Artykuły w czasopismach na temat „Proposed Rank”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Yu, Shicheng, Jiaqing Miao, Guibing Li, Weidong Jin, Gaoping Li, and Xiaoguang Liu. "Tensor Completion via Smooth Rank Function Low-Rank Approximate Regularization." Remote Sensing 15, no. 15 (2023): 3862. http://dx.doi.org/10.3390/rs15153862.

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In recent years, the tensor completion algorithm has played a vital part in the reconstruction of missing elements within high-dimensional remote sensing image data. Due to the difficulty of tensor rank computation, scholars have proposed many substitutions of tensor rank. By introducing the smooth rank function (SRF), this paper proposes a new tensor rank nonconvex substitution function that performs adaptive weighting on different singular values to avoid the performance deficiency caused by the equal treatment of all singular values. On this basis, a novel tensor completion model that minim

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Patidar, Deepak, and Rajeev G. Vishwakarma. "Webometrics Rank Inspection: Proposed for Business Domain." International Journal of Computer Applications 77, no. 3 (2013): 1–5. http://dx.doi.org/10.5120/13371-0973.

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Zaher, Hegazy, and Ahmed Hafez. "A NOVEL TOURISM COMPETITIVENESS RANK." International Journal of Research -GRANTHAALAYAH 5, no. 5 (2017): 164–69. http://dx.doi.org/10.29121/granthaalayah.v5.i5.2017.1848.

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This paper proposes a novel tourism rank to arrange countries in tourism. The proposed rank is based on fifteen factors, the fourteen factors affecting the tourism rank considered in the Travel & Tourism competitiveness reports and the number of tourist arrivals considered in United Nations World Tourism Organization rank. The proposed method gives significant differences compared to the traditional ranks used by the Travel & Tourism competitiveness reports and United Nations World Tourism Organization rank.

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Hegazy, Zaher, and Hafez Ahmed. "A NOVEL TOURISM COMPETITIVENESS RANK." INTERNATIONAL JOURNAL OF RESEARCH- GRANTHAALAYAH 5, no. 5 (2017): 164–69. https://doi.org/10.5281/zenodo.583907.

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This paper proposes a novel tourism rank to arrange countries in tourism. The proposed rank is based on fifteen factors, the fourteen factors affecting the tourism rank considered in the Travel & Tourism competitiveness reports and the number of tourist arrivals considered in United Nations World Tourism Organization rank. The proposed method gives significant differences compared to the traditional ranks used by the Travel & Tourism competitiveness reports and United Nations World Tourism Organization rank.

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Simatou, Aristofania, Panagiotis Sarantis, Evangelos Koustas, Athanasios G. Papavassiliou, and Michalis V. Karamouzis. "The Role of the RANKL/RANK Axis in the Prevention and Treatment of Breast Cancer with Immune Checkpoint Inhibitors and Anti-RANKL." International Journal of Molecular Sciences 21, no. 20 (2020): 7570. http://dx.doi.org/10.3390/ijms21207570.

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The receptor activator of nuclear factor-κB (RANK) and the RANK ligand (RANKL) were reported in the regulation of osteoclast differentiation/activation and bone homeostasis. Additionally, the RANKL/RANK axis is a significant mediator of progesterone-driven mammary epithelial cell proliferation, potentially contributing to breast cancer initiation and progression. Moreover, several studies supported the synergistic effect of RANK and epidermal growth factor receptor (EGFR) and described RANK’s involvement in epidermal growth factor receptor 2 (ERBB2)-positive carcinogenesis. Consequently, anti-

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6

Bepari, Bikash, Shubham Kumar, Awanish Tiwari, Divyam, and Sharjil Ahmar. "An Iterative Transient Rank Aggregation Technique for Mitigation of Rank Reversal." International Journal of Synthetic Emotions 9, no. 1 (2018): 40–50. http://dx.doi.org/10.4018/ijse.2018010104.

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With the advent of decision science, significant elucidation has been sought in the literature of multi criteria decision making. Often, it is observed that for the same MCDM problem, different methods fetch way-apart ranks and the phenomenon leads to rank reversal. To alleviate this problem, different methodologies like the Borda rule, the Copeland method, the Condorcet method, the statistical Thurstone scaling, and linear programming methods are readily available in the literature. In connection with the same, the authors proposed a novel technique to aggregate the ranks laid by different me

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7

Bayat, Niloofar, Cody Morrin, Yuheng Wang, and Vishal Misra. "Rank estimation for (approximately) low-rank matrices." ACM SIGMETRICS Performance Evaluation Review 49, no. 2 (2022): 30–32. http://dx.doi.org/10.1145/3512798.3512810.

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In observational data analysis, e.g., causal inference, one often encounters data sets that are noisy and incomplete, but come from inherently "low rank" (or correlated) systems. Examples include user ratings of movies/products and term frequency matrices for documents amongst others. In such analysis, estimating the approximate rank of the data sets serves an important function of delineating the signal from the noise. In this paper, we propose a technique to estimate the rank of observational data matrices, compare it to previously proposed techniques, and make a specific methodological cont

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Balogun, Abdullateef O., Shuib Basri, Saipunidzam Mahamad, et al. "Empirical Analysis of Rank Aggregation-Based Multi-Filter Feature Selection Methods in Software Defect Prediction." Electronics 10, no. 2 (2021): 179. http://dx.doi.org/10.3390/electronics10020179.

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Selecting the most suitable filter method that will produce a subset of features with the best performance remains an open problem that is known as filter rank selection problem. A viable solution to this problem is to independently apply a mixture of filter methods and evaluate the results. This study proposes novel rank aggregation-based multi-filter feature selection (FS) methods to address high dimensionality and filter rank selection problem in software defect prediction (SDP). The proposed methods combine rank lists generated by individual filter methods using rank aggregation mechanisms

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9

Robin, Jean-Marc, and Richard J. Smith. "TESTS OF RANK." Econometric Theory 16, no. 2 (2000): 151–75. http://dx.doi.org/10.1017/s0266466600162012.

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This paper considers tests for the rank of a matrix for which a root-T consistent estimator is available. However, in contrast to tests associated with the minimum chi-square and asymptotic least squares principles, the estimator's asymptotic variance matrix is not required to be either full or of known rank. Test statistics based on certain estimated characteristic roots are proposed whose limiting distributions are a weighted sum of independent chi-squared variables. These weights may be simply estimated, yielding convenient estimators for the limiting distributions of the proposed statistic

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10

Kenneth, Asanya C., Danjuma Jibasen, and Yohana V. Mbaga. "A Modified Ideal Rank Index Number Formula." International Journal of Development Mathematics (IJDM) 1, no. 2 (2024): 169–78. http://dx.doi.org/10.62054/ijdm/0102.13.

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This paper proposes an index number method called Asanya-Jibasen-Mbaga index, the method satisfied Laspeyre’s and Paasche’s bounding test which is unbiased compared to existing methods. The proposed index methods used expenditure/quantity and rank as weights and it satisfied all the tests of consistency of elementary index formulae. The proposed method was found to approximate the Fisher’s Ideal Price index (PF). The proposed method is an improvement on Jibasen –Gazali-Asanya rank price index. Numerical analyses conducted at low and high levels of aggregation revealed that the Jibasen-Gazali-A

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11

Thonet, Thibaut, Yagmur Gizem Cinar, Eric Gaussier, Minghan Li, and Jean-Michel Renders. "Listwise Learning to Rank Based on Approximate Rank Indicators." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8494–502. http://dx.doi.org/10.1609/aaai.v36i8.20826.

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We study here a way to approximate information retrieval metrics through a softmax-based approximation of the rank indicator function. Indeed, this latter function is a key component in the design of information retrieval metrics, as well as in the design of the ranking and sorting functions. Obtaining a good approximation for it thus opens the door to differentiable approximations of many evaluation measures that can in turn be used in neural end-to-end approaches. We first prove theoretically that the approximations proposed are of good quality, prior to validate them experimentally on both

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Kang, Ji-Soo, Dong-Hoon Shin, Ji-Won Baek, and Kyungyong Chung. "Activity Recommendation Model Using Rank Correlation for Chronic Stress Management." Applied Sciences 9, no. 20 (2019): 4284. http://dx.doi.org/10.3390/app9204284.

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Korean people are exposed to stress due to the constant competitive structure caused by rapid industrialization. As a result, there is a need for ways that can effectively manage stress and help improve quality of life. Therefore, this study proposes an activity recommendation model using rank correlation for chronic stress management. Using Spearman’s rank correlation coefficient, the proposed model finds the correlations between users’ Positive Activity for Stress Management (PASM), Negative Activity for Stress Management (NASM), and Perceived Stress Scale (PSS). Spearman’s rank correlation

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13

Alhumam, Abdulaziz. "Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection." Sensors 21, no. 21 (2021): 7401. http://dx.doi.org/10.3390/s21217401.

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The automatic localization of software faults plays a critical role in assisting software professionals in fixing problems quickly. Despite various existing models for fault tolerance based on static features, localization is still challenging. By considering the dynamic features, the capabilities of the fault recognition models will be significantly enhanced. The current study proposes a model that effectively ranks static and dynamic parameters through Aggregation-Based Neural Ranking (ABNR). The proposed model includes rank lists produced by self-attention layers using rank aggregation mech

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14

Kuchukov, V. A., M. G. Babenko, and N. N. Kucherov. "Investigating the rank of the number in a residue number system." Sovremennaya nauka i innovatsii, no. 2 (42) (2023): 41–49. http://dx.doi.org/10.37493/2307-910x.2023.2.4.

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The rank of a number in a residue number system indicates the count of transitions through a range when a number is converted to a positional number system and allows for more efficient non-modular operations and detection of values out of range. The main approach to calculate the rank is the use of the Chinese Remainder Theorem. In this article the approach which allows to compute the rank using a set of special numbers for which ranks are computed in advance is proposed. The simulation of the considered methods is done in the Python programming language. The results are analyzed and recommen

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15

Ansari, Mohd Zeeshan, and M. M. Sufyan Beg. "Improved Fuzzy Rank Aggregation." International Journal of Rough Sets and Data Analysis 5, no. 4 (2018): 74–87. http://dx.doi.org/10.4018/ijrsda.2018100105.

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Rank aggregation is applied on the web to build various applications like meta-search engines, consumer reviews classification, and recommender systems. Meta-searching is the generation of a single list from a collection of the results produced by multiple search engines, together using a rank aggregation technique. It is an efficient and cost-effective technique to retrieve quality results from the internet. The quality of results produced by a meta-searching relies upon the efficiency of rank aggregation technique applied. An effective rank aggregation technique assigns the rank to a documen

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16

Gao, Bin, and P. A. Absil. "A Riemannian rank-adaptive method for low-rank matrix completion." Computational Optimization and Applications 81, no. 1 (2021): 67–90. http://dx.doi.org/10.1007/s10589-021-00328-w.

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AbstractThe low-rank matrix completion problem can be solved by Riemannian optimization on a fixed-rank manifold. However, a drawback of the known approaches is that the rank parameter has to be fixed a priori. In this paper, we consider the optimization problem on the set of bounded-rank matrices. We propose a Riemannian rank-adaptive method, which consists of fixed-rank optimization, rank increase step and rank reduction step. We explore its performance applied to the low-rank matrix completion problem. Numerical experiments on synthetic and real-world datasets illustrate that the proposed r

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17

Yano, Ken, and Takayuki Suyama. "A Novel Fixed Low-Rank Constrained EEG Spatial Filter Estimation with Application to Movie-Induced Emotion Recognition." Computational Intelligence and Neuroscience 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/6734720.

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This paper proposes a novel fixed low-rank spatial filter estimation for brain computer interface (BCI) systems with an application that recognizes emotions elicited by movies. The proposed approach unifies such tasks as feature extraction, feature selection, and classification, which are often independently tackled in a “bottom-up” manner, under a regularized loss minimization problem. The loss function is explicitly derived from the conventional BCI approach and solves its minimization by optimization with a nonconvex fixed low-rank constraint. For evaluation, an experiment was conducted to

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18

Chaudhari, Kalyani, Shruti Oza, Diksha Chopade, Pranali Yawle, Abhishek Gandhar, and Yogesh Kute. "Low rank sparse coefficient based nuchal translucency image de-noising." Journal of Information and Optimization Sciences 45, no. 2 (2024): 333–41. http://dx.doi.org/10.47974/jios-1550.

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The process of eliminating distortion or noise from an image is known as image de-noising. Random noise is introduced to ultrasonic imaging, resulting in reduced contrast in the images. For Nuchal translucency (NT) detection, image de-noising is a crucial stage. Although deep-learning methods have been extensively studied for this problem and have shown compelling results, most networks may result in disappearing or inflating gradients and need more memory and time to attain a spectacular performance. To achieve better overall framework optimization, Novel Methodology of anisotropic filtering

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Jaisankar, R., and M. Siva. "The Fuzzified Log-Rank Test Procedure for Comparing More Than Two Groups." Indian Journal Of Science And Technology 17, no. 37 (2024): 3834–39. http://dx.doi.org/10.17485/ijst/v17i37.2244.

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Objectives: To extend the conventional log-rank test to handle fuzzy data, accommodating the inherent uncertainty and vagueness in illustrative data. To create a statistical procedure that can compare survival distributions across more than two groups. Methods: The log-rank test is a familiar non-parametric methodology used to compare the survival experiences of two or more groups of subjects. This study applies fuzzification procedures to survival data, transforming precise survival times and event indicators into fuzzy numbers. Findings: When fuzziness is attributed to the survival data, the

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20

Ahmed, Mohammed, Nurulhaque Usmani, Javed Khan, Shahnawaz Khan, and Imran Shaikh. "Garbage Profiling A Proposed System to rank localities based on waste segregation." International Journal of Computer Sciences and Engineering 7, no. 2 (2019): 852–55. http://dx.doi.org/10.26438/ijcse/v7i2.852855.

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Liu, Hongyi, Hanyang Li, Zebin Wu, and Zhihui Wei. "Hyperspectral Image Recovery Using Non-Convex Low-Rank Tensor Approximation." Remote Sensing 12, no. 14 (2020): 2264. http://dx.doi.org/10.3390/rs12142264.

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Low-rank tensors have received more attention in hyperspectral image (HSI) recovery. Minimizing the tensor nuclear norm, as a low-rank approximation method, often leads to modeling bias. To achieve an unbiased approximation and improve the robustness, this paper develops a non-convex relaxation approach for low-rank tensor approximation. Firstly, a non-convex approximation of tensor nuclear norm (NCTNN) is introduced to the low-rank tensor completion. Secondly, a non-convex tensor robust principal component analysis (NCTRPCA) method is proposed, which aims at exactly recovering a low-rank tens

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Shi, Jiarong, Wei Yang, Longquan Yong, and Xiuyun Zheng. "Low-Rank Representation for Incomplete Data." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/439417.

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Low-rank matrix recovery (LRMR) has been becoming an increasingly popular technique for analyzing data with missing entries, gross corruptions, and outliers. As a significant component of LRMR, the model of low-rank representation (LRR) seeks the lowest-rank representation among all samples and it is robust for recovering subspace structures. This paper attempts to solve the problem of LRR with partially observed entries. Firstly, we construct a nonconvex minimization by taking the low rankness, robustness, and incompletion into consideration. Then we employ the technique of augmented Lagrange

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Yin, Shuai, Yanfeng Sun, Junbin Gao, Yongli Hu, Boyue Wang, and Baocai Yin. "Robust Image Representation via Low Rank Locality Preserving Projection." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (2021): 1–22. http://dx.doi.org/10.1145/3434768.

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Locality preserving projection (LPP) is a dimensionality reduction algorithm preserving the neighhorhood graph structure of data. However, the conventional LPP is sensitive to outliers existing in data. This article proposes a novel low-rank LPP model called LR-LPP. In this new model, original data are decomposed into the clean intrinsic component and noise component. Then the projective matrix is learned based on the clean intrinsic component which is encoded in low-rank features. The noise component is constrained by the ℓ 1 -norm which is more robust to outliers. Finally, LR-LPP model is ex

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Shoukry, Nadeen, Mohamed A. Abd El Ghany, and Mohammed A. M. Salem. "Multi-Modal Long-Term Person Re-Identification Using Physical Soft Bio-Metrics and Body Figure." Applied Sciences 12, no. 6 (2022): 2835. http://dx.doi.org/10.3390/app12062835.

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Person re-identification is the task of recognizing a subject across different non-overlapping cameras across different views and times. Most state-of-the-art datasets and proposed solutions tend to address the problem of short-term re-identification. Those models can re-identify a person as long as they are wearing the same clothes. The work presented in this paper addresses the task of long-term re-identification. Therefore, the proposed model is trained on a dataset that incorporates clothes variation. This paper proposes a multi-modal person re-identification model. The first modality incl

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Schaad, N. W., A. K. Vidaver, G. H. Lacy, K. Rudolph, and J. B. Jones. "Evaluation of Proposed Amended Names of Several Pseudomonads and Xanthomonads and Recommendations." Phytopathology® 90, no. 3 (2000): 208–13. http://dx.doi.org/10.1094/phyto.2000.90.3.208.

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In 1980, over 90% of all plant-pathogenic pseudomonads and xanthomonads were lumped into Pseudomonas syringae and Xanthomonas campestris, respectively, as pathovars. The term “pathovar” was created to preserve the name of plant pathogens, but has no official standing in nomenclature. Proposals to elevate and rename several pathovars of the genera Pseudomonas and Xanthomonas to the rank of species has caused great confusion in the literature. We believe the following changes have merit and expect to adopt them for publication in a future American Phytopathological Society Laboratory Guide for I

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Pan, Yan, Hanjiang Lai, Cong Liu, Yong Tang, and Shuicheng Yan. "Rank Aggregation via Low-Rank and Structured-Sparse Decomposition." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 760–66. http://dx.doi.org/10.1609/aaai.v27i1.8556.

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Rank aggregation, which combines multiple individual rank lists toobtain a better one, is a fundamental technique in various applications such as meta-search and recommendation systems. Most existing rank aggregation methods blindly combine multiple rank lists with possibly considerable noises, which often degrades their performances. In this paper, we propose a new model for robust rank aggregation (RRA) via matrix learning, which recovers a latent rank list from the possibly incomplete and noisy input rank lists. In our model, we construct a pairwise comparison matrix to encode the order inf

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Celik, Nuri. "Some Cubic Rank Transmuted Distributions." Journal of Applied Mathematics, Statistics and Informatics 14, no. 2 (2018): 27–43. http://dx.doi.org/10.2478/jamsi-2018-0011.

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Abstract In this article, we introduce some examples of cubic rank transmuted distributions proposed by Granzatto et al. (2017). The statistical aspects of the introduced distributions such as probability density functions, hazard rate functions and reliability functions are studied. The maximum likelihood estimation method is used in order to estimate the parameters of interest. Finally, real data examples are applied for the illustration of these distributions.

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Xuan, Lv, Ma Zezhong, and Liu Qing. "Low-Rank Optimization Dictionary Training for Image Classification." MATEC Web of Conferences 173 (2018): 03034. http://dx.doi.org/10.1051/matecconf/201817303034.

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Bag-of-words model has been extremely popular in image categorization. The method of constructing the dictionary is important. In this paper a category constrained low-rank optimization dictionary training approach is proposed for the dictionary construction. Through the low-rank optimization, the rank of the coefficient matrix constructed by same category images is minimized. Experimental results show that the proposed method can obtain better performance on two standard image databases (Caltech-101 and Caltech-256) than not employing the category constrained low-rank optimization.

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Gao, Yi, Xuanli Han, and Mingde Ma. "Recovery of low-rank matrices based on the rank null space properties." International Journal of Wavelets, Multiresolution and Information Processing 15, no. 04 (2017): 1750032. http://dx.doi.org/10.1142/s0219691317500321.

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This paper first discusses the relationship between the rank null space property (NSP) and the nuclear norm minimization. Several versions of the rank NSP, i.e. the stable rank NSP, robust rank NSP and Frobenius robust rank NSP are proposed, and their equivalent forms are derived. At the same time, it is shown that the stable rank NSP is weaker than the rank restricted isometry property (RIP) to recover the low-rank matrices via the nuclear norm minimization. Finally, the rank NSP is extended to the case of Schatten-[Formula: see text] NSP for [Formula: see text], and the solutions to the Scha

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Jin, Tao, Pan Xu, Quanquan Gu, and Farzad Farnoud. "Rank Aggregation via Heterogeneous Thurstone Preference Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4353–60. http://dx.doi.org/10.1609/aaai.v34i04.5860.

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We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranked data, which can take the accuracy levels of different users into account. By allowing different noise distributions, the proposed HTM model maintains the generality of Thurstone's original framework, and as such, also extends the Bradley-Terry-Luce (BTL) model for pairwise comparisons to heterogeneous populations of users. Under this framework, we also propose a rank aggregation algorithm based on alternating gradient descent to estimate the underlying item scores and accuracy levels of different users simultaneously fro

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Lestari, Sri, Teguh Bharata Adji, and Adhistya Erna Permanasari. "WP-Rank: Rank Aggregation based Collaborative Filtering Method in Recommender System." International Journal of Engineering & Technology 7, no. 4.40 (2018): 193–97. http://dx.doi.org/10.14419/ijet.v7i4.40.24431.

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Collaborative filtering with a traditional rating-based approach uses interaction records between users and systems to measure similarity, prediction, and generate recommendations. However, traditional rating based cannot capture user preferences of different products. To overcome this issue, the ranking based approach, such as the Borda method has been used. The method takes advantage of rating data to determine the position of the product in the list of preferences as the basis for determining product points. However, the list of the preferences which is merely based on rating data, results

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Sushilkumar Chavhan. "Embedding Hybrid Evolutionary Approach for Learning-to-Rank Computation for the Selection of Features Using Machine Learning." Communications on Applied Nonlinear Analysis 31, no. 2s (2024): 454–69. http://dx.doi.org/10.52783/cana.v31.660.

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Our study proposes a novel model for retrieving objects that utilizes learning-to-rank with L2 regularization. We employed an evolutionary-based simulated annealing technique to select the most informative features for our system and utilized a standardized regulation technique to handle the dropout of active features. Learning to rank is a well-researched area in machine learning and finds application in recommendation systems and search engines. Our study aims to introduce a new approach to feature selection for the learning-to-rank information retrieval model. By dropping inactive features

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Liu, Zuozhi, JinJian Wu, and Jianpeng Wang. "An Improved Extreme Learning Machine Based on Full Rank Cholesky Factorization." MATEC Web of Conferences 246 (2018): 03018. http://dx.doi.org/10.1051/matecconf/201824603018.

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Extreme learning machine (ELM) is a new novel learning algorithm for generalized single-hidden layer feedforward networks (SLFNs). Although it shows fast learning speed in many areas, there is still room for improvement in computational cost. To address this issue, this paper proposes an improved ELM (FRCFELM) which employs the full rank Cholesky factorization to compute output weights instead of traditional SVD. In addition, this paper proves in theory that the proposed FRCF-ELM has lower computational complexity. Experimental results over some benchmark applications indicate that the propose

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Zhu, E., M. Xu, and D. Pi. "A Novel Robust Principal Component Analysis Algorithm of Nonconvex Rank Approximation." Mathematical Problems in Engineering 2020 (September 30, 2020): 1–17. http://dx.doi.org/10.1155/2020/9356935.

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Noise exhibits low rank or no sparsity in the low-rank matrix recovery, and the nuclear norm is not an accurate rank approximation of low-rank matrix. In the present study, to solve the mentioned problem, a novel nonconvex approximation function of the low-rank matrix was proposed. Subsequently, based on the nonconvex rank approximation function, a novel model of robust principal component analysis was proposed. Such model was solved with the alternating direction method, and its convergence was verified theoretically. Subsequently, the background separation experiments were performed on the W

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Pynnonen, Seppo. "Non-Parametric Statistic for Testing Cumulative Abnormal Stock Returns." Journal of Risk and Financial Management 15, no. 4 (2022): 149. http://dx.doi.org/10.3390/jrfm15040149.

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Due to the non-normality of stock returns, nonparametric rank tests are gaining accceptance relative to parametric tests in financial economics event studies. In rank tests, financial assets’ multiple day cumulative abnormal returns (CARs) are replaced by cumulated ranks. This paper proposes modifications to the existing approaches to improve robustness to cross-sectional correlation of returns arising from calendar time overlapping event windows. Simulations show that the proposed rank test is well specified in testing CARs and is robust towards both complete and partial overlapping event win

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Lou, Xi-Cheng, and Xin Feng. "Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation." Computational and Mathematical Methods in Medicine 2021 (September 28, 2021): 1–16. http://dx.doi.org/10.1155/2021/1544955.

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A multimodal medical image fusion algorithm based on multiple latent low-rank representation is proposed to improve imaging quality by solving fuzzy details and enhancing the display of lesions. Firstly, the proposed method decomposes the source image repeatedly using latent low-rank representation to obtain several saliency parts and one low-rank part. Secondly, the VGG-19 network identifies the low-rank part’s features and generates the weight maps. Then, the fused low-rank part can be obtained by making the Hadamard product of the weight maps and the source images. Thirdly, the fused salien

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Shen, Xiang-Jun, Stanley Ebhohimhen Abhadiomhen, Yang Yang, Zhifeng Liu, and Sirui Tian. "Edge Structure Learning via Low Rank Residuals for Robust Image Classification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 2236–44. http://dx.doi.org/10.1609/aaai.v37i2.25318.

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Traditional low-rank methods overlook residuals as corruptions, but we discovered that low-rank residuals actually keep image edges together with corrupt components. Therefore, filtering out such structural information could hamper the discriminative details in images, especially in heavy corruptions. In order to address this limitation, this paper proposes a novel method named ESL-LRR, which preserves image edges by finding image projections from low-rank residuals. Specifically, our approach is built in a manifold learning framework where residuals are regarded as another view of image data.

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Isha, Mahajan. "Extended Weighted Page Rank Based on VOL by Finding User Activities Time and Page Reading Time, Storing them Directly on Search Engine Database Server." International Journal of Engineering Works (ISSN:2409-2770) 4, no. 2 (2017): 41–48. https://doi.org/10.5281/zenodo.376487.

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Searching on the web can be considered as a process of user enters the query and search system returns a set of most relevant pages in response to user’s query. But results returned are not mostly relevant to user’s query and ranking of the pages are not efficient according to user requirement. In order to improve the precision of ranking of the web pages, after analyzing the different algorithms like Page Rank, Weighted Page Rank, Page Rank based on VOL, Weighted Page Rank algorithm based on VOL. In this paper, we are proposing enhancement by including “User Activities Time” and “Page Reading

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Ghosh, Ashish, and Mrinal Kanti Das. "Non-dominated Rank based Sorting Genetic Algorithms." Fundamenta Informaticae 83, no. 3 (2008): 231–52. https://doi.org/10.3233/fun-2008-83301.

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In this paper a new concept of ranking among the solutions of the same front, along with elite preservation mechanism and ensuring diversity through the nearest neighbor method is proposed for multi-objective genetic algorithms. This algorithm is applied on a set of benchmark multi-objective test problems and the results are compared with that of NSGA-II (a similar algorithm). The proposed algorithm is seen to over perform the existing algorithm. More specifically, the new approach has been used to solve the deceptive multi-objective optimization problems in a better way.

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Liang, Hao, Hai-Tang Guan, Stanley Ebhohimhen Abhadiomhen, and Li Yan. "Robust Spectral Clustering via Low-Rank Sample Representation." Applied Computational Intelligence and Soft Computing 2022 (April 29, 2022): 1–11. http://dx.doi.org/10.1155/2022/7540956.

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Traditional clustering methods neglect the data quality and perform clustering directly on the original data. Therefore, their performance can easily deteriorate since real-world data would usually contain noisy data samples in high-dimensional space. In order to resolve the previously mentioned problem, a new method is proposed, which builds on the approach of low-rank representation. The proposed approach first learns a low-rank coefficient matrix from data by exploiting the data’s self-expressiveness property. Then, a regularization term is introduced to ensure that the representation coeff

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An, Jinliang, Jinhui Lei, Yuzhen Song, Xiangrong Zhang, and Jinmei Guo. "Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction." Remote Sensing 11, no. 12 (2019): 1485. http://dx.doi.org/10.3390/rs11121485.

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Dimensionality reduction is an essential and important issue in hyperspectral image processing. With the advantages of preserving the spatial neighborhood information and the global structure information, tensor analysis and low rank representation have been widely considered in this field and yielded satisfactory performance. In available tensor- and low rank-based methods, how to construct appropriate tensor samples and determine the optimal rank of hyperspectral images along each mode are still challenging issues. To address these drawbacks, an unsupervised tensor-based multiscale low rank

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Zeng, Xueying, Lixin Shen, Yuesheng Xu, and Jian Lu. "Matrix completion via minimizing an approximate rank." Analysis and Applications 17, no. 05 (2019): 689–713. http://dx.doi.org/10.1142/s0219530519400025.

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The low rank matrix completion problem which aims to recover a matrix from that having missing entries has received much attention in many fields such as image processing and machine learning. The rank of a matrix may be measured by the [Formula: see text] norm of the vector of its singular values. Due to the nonconvexity and discontinuity of the [Formula: see text] norm, solving the low rank matrix completion problem which is clearly NP hard suffers from computational challenges. In this paper, we propose a constrained matrix completion model in which a novel nonconvex continuous rank surroga

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Xu, Jiucheng, Yihao Cheng, and Yuanyuan Ma. "Weighted Schatten p-Norm Low Rank Error Constraint for Image Denoising." Entropy 23, no. 2 (2021): 158. http://dx.doi.org/10.3390/e23020158.

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Traditional image denoising algorithms obtain prior information from noisy images that are directly based on low rank matrix restoration, which pays little attention to the nonlocal self-similarity errors between clear images and noisy images. This paper proposes a new image denoising algorithm based on low rank matrix restoration in order to solve this problem. The proposed algorithm introduces the non-local self-similarity error between the clear image and noisy image into the weighted Schatten p-norm minimization model using the non-local self-similarity of the image. In addition, the low r

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Xu, Zhengqin, Yulun Zhang, Chao Ma, et al. "LERE: Learning-Based Low-Rank Matrix Recovery with Rank Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 16228–36. http://dx.doi.org/10.1609/aaai.v38i14.29557.

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A fundamental task in the realms of computer vision, Low-Rank Matrix Recovery (LRMR) focuses on the inherent low-rank structure precise recovery from incomplete data and/or corrupted measurements given that the rank is a known prior or accurately estimated. However, it remains challenging for existing rank estimation methods to accurately estimate the rank of an ill-conditioned matrix. Also, existing LRMR optimization methods are heavily dependent on the chosen parameters, and are therefore difficult to adapt to different situations. Addressing these issues, A novel LEarning-based low-rank mat

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Hollister, Brad E., and Alex Pang. "Uncertainty Rank for Streamline Ensembles." Journal of Imaging Science and Technology 64, no. 2 (2020): 20504–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.2.020504.

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Abstract Traditional spaghetti plots from ensemble data provide no explicit information as to the uncertainty of the realization flow paths. While intuitive assessment can be used when visualizing streamline density directly in such a plot, the display is often cluttered and difficult to interpret. The authors present a method to measure uncertainty and visualize member streamlines from an ensemble of vector fields. The method incorporates velocity probability density as a feature along each member streamline. The authors show visualizations of two different data sets using the proposed method

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Sobolev, Konstantin, Dmitry Ermilov, Anh-Huy Phan, and Andrzej Cichocki. "PARS: Proxy-Based Automatic Rank Selection for Neural Network Compression via Low-Rank Weight Approximation." Mathematics 10, no. 20 (2022): 3801. http://dx.doi.org/10.3390/math10203801.

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Low-rank matrix/tensor decompositions are promising methods for reducing the inference time, computation, and memory consumption of deep neural networks (DNNs). This group of methods decomposes the pre-trained neural network weights through low-rank matrix/tensor decomposition and replaces the original layers with lightweight factorized layers. A main drawback of the technique is that it demands a great amount of time and effort to select the best ranks of tensor decomposition for each layer in a DNN. This paper proposes a Proxy-based Automatic tensor Rank Selection method (PARS) that utilizes

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Liu, Yang, Wissam Sid-Lakhdar, Elizaveta Rebrova, Pieter Ghysels, and Xiaoye Sherry Li. "A parallel hierarchical blocked adaptive cross approximation algorithm." International Journal of High Performance Computing Applications 34, no. 4 (2020): 394–408. http://dx.doi.org/10.1177/1094342020918305.

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This article presents a low-rank decomposition algorithm based on subsampling of matrix entries. The proposed algorithm first computes rank-revealing decompositions of submatrices with a blocked adaptive cross approximation (BACA) algorithm, and then applies a hierarchical merge operation via truncated singular value decompositions (H-BACA). The proposed algorithm significantly improves the convergence of the baseline ACA algorithm and achieves reduced computational complexity compared to the traditional decompositions such as rank-revealing QR. Numerical results demonstrate the efficiency, ac

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Li, Guibing, Weidong Jin, Jiaqing Miao, et al. "Remote Sensing Image of The Landsat 8–9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace Function." Entropy 25, no. 3 (2023): 523. http://dx.doi.org/10.3390/e25030523.

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Utilizing low-rank prior data in compressed sensing (CS) schemes for Landsat 8–9 remote sensing images (RSIs) has recently received widespread attention. Nevertheless, most CS algorithms focus on the sparsity of an RSI and ignore its low-rank (LR) nature. Therefore, this paper proposes a new CS reconstruction algorithm for Landsat 8–9 remote sensing images based on a non-local optimization framework (NLOF) that is combined with non-convex Laplace functions (NCLF) used for the low-rank approximation (LAA). Since the developed algorithm is based on an approximate low-rank model of the Laplace fu

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Gao, Wenyun, Xiaoyun Li, Sheng Dai, Xinghui Yin, and Stanley Ebhohimhen Abhadiomhen. "Recursive Sample Scaling Low-Rank Representation." Journal of Mathematics 2021 (December 27, 2021): 1–14. http://dx.doi.org/10.1155/2021/2999001.

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The low-rank representation (LRR) method has recently gained enormous popularity due to its robust approach in solving the subspace segmentation problem, particularly those concerning corrupted data. In this paper, the recursive sample scaling low-rank representation (RSS-LRR) method is proposed. The advantage of RSS-LRR over traditional LRR is that a cosine scaling factor is further introduced, which imposes a penalty on each sample to minimize noise and outlier influence better. Specifically, the cosine scaling factor is a similarity measure learned to extract each sample’s relationship with

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Li, Chunlei, Chaodie Liu, Zhoufeng Liu, Ruimin Yang, and Yun Huang. "Fabric defect detection method based on cascaded low-rank decomposition." International Journal of Clothing Science and Technology 32, no. 4 (2020): 483–98. http://dx.doi.org/10.1108/ijcst-03-2019-0037.

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PurposeThe purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile manufacturing.Design/methodology/approachThis paper proposed a fabric defect detection algorithm based on cascaded low-rank decomposition. First, the constructed Gabor feature matrix is divided into a low-rank matrix and sparse matrix using low-rank decomposition technique, and the sparse matrix is used as priori matrix where higher values indicate a higher probability of abnormality. Second, we conducted the se

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