scholarly journals How Many Pairwise Preferences Do We Need to Rank a Graph Consistently?

2019 ◽  
Vol 33 ◽  
pp. 4830-4837
Author(s):  
Aadirupa Saha ◽  
Rakesh Shivanna ◽  
Chiranjib Bhattacharyya
Keyword(s):  
Optimal Recovery ◽  
Learning Problems ◽  
Sample Complexity ◽  
Strong Product ◽  
Large N ◽  
Dense Graphs ◽  
Node Connectivity ◽  
Real World Datasets ◽  
Statistical Consistency ◽  
First Time

We consider the problem of optimal recovery of true ranking of n items from a randomly chosen subset of their pairwise preferences. It is well known that without any further assumption, one requires a sample size of Ω(n2) for the purpose. We analyze the problem with an additional structure of relational graph G([n],E) over the n items added with an assumption of locality: Neighboring items are similar in their rankings. Noting the preferential nature of the data, we choose to embed not the graph, but, its strong product to capture the pairwise node relationships. Furthermore, unlike existing literature that uses Laplacian embedding for graph based learning problems, we use a richer class of graph embeddings—orthonormal representations—that includes (normalized) Laplacian as its special case. Our proposed algorithm, Pref-Rank, predicts the underlying ranking using an SVM based approach using the chosen embedding of the product graph, and is the first to provide statistical consistency on two ranking losses: Kendall’s tau and Spearman’s footrule, with a required sample complexity of O(n2χ(G¯))⅔ pairs, χ(G¯) being the chromatic number of the complement graph G¯. Clearly, our sample complexity is smaller for dense graphs, with χ(G¯) characterizing the degree of node connectivity, which is also intuitive due to the locality assumption e.g. O(n4/3) for union of k-cliques, or O(n5/3) for random and power law graphs etc.—a quantity much smaller than the fundamental limit of Ω(n2) for large n. This, for the first time, relates ranking complexity to structural properties of the graph. We also report experimental evaluations on different synthetic and real-world datasets, where our algorithm is shown to outperform the state of the art methods.

scholarly journals Exponential Family Graph Embeddings

2020 ◽  
Vol 34 (04) ◽  
pp. 3357-3364
Author(s):  
Abdulkadir Celikkanat ◽  
Fragkiskos D. Malliaros
Keyword(s):  
Random Walk ◽  
Exponential Family ◽  
Representation Learning ◽  
Learning Problems ◽  
Interaction Patterns ◽  
Network Representation ◽  
Learning Tasks ◽  
Learning Techniques ◽  
Real World Datasets ◽  
Low Dimensional

Representing networks in a low dimensional latent space is a crucial task with many interesting applications in graph learning problems, such as link prediction and node classification. A widely applied network representation learning paradigm is based on the combination of random walks for sampling context nodes and the traditional Skip-Gram model to capture center-context node relationships. In this paper, we emphasize on exponential family distributions to capture rich interaction patterns between nodes in random walk sequences. We introduce the generic exponential family graph embedding model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions. We study three particular instances of this model, analyzing their properties and showing their relationship to existing unsupervised learning models. Our experimental evaluation on real-world datasets demonstrates that the proposed techniques outperform well-known baseline methods in two downstream machine learning tasks.

scholarly journals Adaptive Double-Exploration Tradeoff for Outlier Detection

2020 ◽  
Vol 34 (04) ◽  
pp. 6837-6844
Author(s):  
Xiaojin Zhang ◽  
Honglei Zhuang ◽  
Shengyu Zhang ◽  
Yuan Zhou
Keyword(s):  
Confidence Interval ◽  
Outlier Detection ◽  
Real World ◽  
Efficient Algorithm ◽  
Experimental Results ◽  
Sample Complexity ◽  
Bandit Problem ◽  
Real World Datasets ◽  
Synthetic Datasets ◽  
The Individual

We study a variant of the thresholding bandit problem (TBP) in the context of outlier detection, where the objective is to identify the outliers whose rewards are above a threshold. Distinct from the traditional TBP, the threshold is defined as a function of the rewards of all the arms, which is motivated by the criterion for identifying outliers. The learner needs to explore the rewards of the arms as well as the threshold. We refer to this problem as "double exploration for outlier detection". We construct an adaptively updated confidence interval for the threshold, based on the estimated value of the threshold in the previous rounds. Furthermore, by automatically trading off exploring the individual arms and exploring the outlier threshold, we provide an efficient algorithm in terms of the sample complexity. Experimental results on both synthetic datasets and real-world datasets demonstrate the efficiency of our algorithm.

Semi-Supervised Local Fisher Discriminant Analysis Based on Reconstruction Probability Class

2015 ◽  
Vol 29 (02) ◽  
pp. 1550007 ◽  
Author(s):  
Yintong Wang ◽  
Jiandong Wang ◽  
Haiyan Chen ◽  
Bo Sun
Keyword(s):  
Discriminant Analysis ◽  
Supervised Learning ◽  
Unlabeled Data ◽  
Learning Problems ◽  
Fisher Discriminant Analysis ◽  
Partially Labeled Data ◽  
Fisher Discriminant ◽  
Analysis Process ◽  
Local Fisher Discriminant Analysis ◽  
Real World Datasets

Fisher discriminant analysis (FDA) is a classic supervised dimensionality reduction method in statistical pattern recognition. FDA can maximize the scatter between different classes, while minimizing the scatter within each class. As it only utilizes the labeled data and ignores the unlabeled data in the analysis process of FDA, it cannot be used to solve the unsupervised learning problems. Its performance is also very poor in dealing with semi-supervised learning problems in some cases. Recently, several semi-supervised learning methods as an extension of FDA have proposed. Most of these methods solve the semi-supervised problem by using a tradeoff parameter that evaluates the ratio of the supervised and unsupervised methods. In this paper, we propose a general semi-supervised dimensionality learning idea for the partially labeled data, namely the reconstruction probability class of labeled and unlabeled data. Based on the probability class optimizes Fisher criterion function, we propose a novel Semi-Supervised Local Fisher Discriminant Analysis (S2LFDA) method. Experimental results on real-world datasets demonstrate its effectiveness compared to the existing similar correlation methods.

scholarly journals Online Multitask Relative Similarity Learning

2017 ◽  
Author(s):  
Shuji Hao ◽  
Peilin Zhao ◽  
Yong Liu ◽  
Steven C. H. Hoi ◽  
Chunyan Miao
Keyword(s):  
Real World ◽  
Learning Algorithm ◽  
Learning Problems ◽  
Similarity Function ◽  
Learning Approaches ◽  
Similarity Learning ◽  
Real World Data ◽  
Real World Datasets ◽  
Online Learning Algorithm ◽  
Relative Similarity

Relative similarity learning~(RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with real-world data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-optimal to solve multiple task learning problems. To overcome these limitations, we propose a scalable RSL framework named OMTRSL (Online Multi-Task Relative Similarity Learning). Specifically, we first develop a simple yet effective online learning algorithm for multi-task relative similarity learning. Then, we also propose an active learning algorithm to save the labeling cost. The proposed algorithms not only enjoy theoretical guarantee, but also show high efficacy and efficiency in extensive experiments on real-world datasets.

scholarly journals Maximum Planar Subgraphs in Dense Graphs

10.37236/3041 ◽  
2013 ◽  
Vol 20 (3) ◽  
Author(s):  
Peter Allen ◽  
Jozef Skokan ◽  
Andreas Würfl
Keyword(s):  
Minimum Degree ◽  
Large N ◽  
Dense Graphs ◽  
Vertex Graph ◽  
Planar Subgraphs

Kühn, Osthus and Taraz showed that for each $\gamma>0$ there exists $C$ such that any $n$-vertex graph with minimum degree $\gamma n$ contains a planar subgraph with at least $2n-C$ edges. We find the optimum value of $C$ for all $\gamma< 1/2$ and sufficiently large $n$.

scholarly journals Using Mechanical Turk to Assess the Effects of Age and Spatial Proximity on Inattentional Blindness

Collabra ◽  
2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Cary R. Stothart ◽  
Walter R. Boot ◽  
Daniel J. Simons
Keyword(s):  
Cognitive Aging ◽  
Inattentional Blindness ◽  
Mechanical Turk ◽  
Spatial Proximity ◽  
Online Data ◽  
Advantages And Disadvantages ◽  
Large N ◽  
Online Data Collection ◽  
First Time ◽  
The Relationship

Few studies have used online data collection to study cognitive aging. We used a large (N = 515) online sample to replicate the findings that inattentional blindness increases with age and with the distance of the unexpected object from the focus of attention. Critically, we assessed whether distance moderates the relationship between age and noticing. We replicated both age and distance effects, but found no age by distance interaction. These findings disconfirm a plausible explanation for age differences in noticing (restricted field of view), while for the first time highlighting the advantages and disadvantages of using Mechanical Turk for the study of cognitive aging.

scholarly journals Thinking Fast and Slow: A CBR Perspective

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Srashti Kaurav ◽  
Devi Ganesan ◽  
Deepak P ◽  
Sutanu Chakraborti
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Human Cognition ◽  
Case Based Reasoning ◽  
Machine Learning Model ◽  
Intuitive Decision Making ◽  
Thinking Process ◽  
Real World Datasets ◽  
First Time ◽  
Fast Decision

In a path-breaking work, Kahneman characterized human cognition as a result of two modes of operation, Fast Thinking and Slow Thinking. Fast thinking involves quick, intuitive decision making and slow thinking is deliberative conscious reasoning. In this paper, for the first time, we draw parallels between this dichotomous model of human cognition and decision making in Case-based Reasoning (CBR). We observe that fast thinking can be operationalized computationally as the fast decision making by a trained machine learning model, or a parsimonious CBR system that uses few attributes. On the other hand, a full-fledged CBR system may be seen as similar to the slow thinking process. We operationalize such computational models of fast and slow thinking and switching strategies, as Models 1 and 2. Further, we explore the adaptation process in CBR as a slow thinking manifestation, leading to Model 3. Through an extensive set of experiments on real-world datasets, we show that such realizations of fast and slow thinking are useful in practice, leading to improved accuracies in decision-making tasks.

scholarly journals A Novel Approach for Detection of Fake News on Social Media Using Metaheuristic Optimization Algorithms

2019 ◽  
Vol 25 (4) ◽  
pp. 62-67 ◽  
Author(s):  
Feyza Altunbey Ozbay ◽  
Bilal Alatas
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Fake News ◽  
Second Stage ◽  
Novel Approach ◽  
Three Stages ◽  
Last Stage ◽  
Real World Datasets ◽  
First Time ◽  
Metaheuristic Optimization Algorithms

Deceptive content is becoming increasingly dangerous, such as fake news created by social media users. Individuals and society have been affected negatively by the spread of low-quality news on social media. The fake and real news needs to be detected to eliminate the disadvantages of social media. This paper proposes a novel approach for fake news detection (FND) problem on social media. Applying this approach, FND problem has been considered as an optimization problem for the first time and two metaheuristic algorithms, the Grey Wolf Optimization (GWO) and Salp Swarm Optimization (SSO) have been adapted to the FND problem for the first time as well. The proposed FND approach consists of three stages. The first stage is data preprocessing. The second stage is adapting GWO and SSO for construction of a novel FND model. The last stage consists of using proposed FND model for testing. The proposed approach has been evaluated using three different real-world datasets. The results have been compared with seven supervised artificial intelligence algorithms. The results show GWO algorithm has the best performance in comparison with SSO algorithm and the other artificial intelligence algorithms. GWO seems to be efficiently used for solving different types of social media problems.

A comparison of methods used to measure the importance of service attributes

2014 ◽  
Vol 56 (3) ◽  
pp. 283-296 ◽  
Author(s):  
Elena Pokryshevskaya ◽  
Evgeny Antipov
Keyword(s):  
Decision Maker ◽  
Shapley Value ◽  
Real World ◽  
Discriminatory Power ◽  
Comparison Of Methods ◽  
Empirical Comparison ◽  
Real World Datasets ◽  
The Stability ◽  
Value Decomposition ◽  
First Time

In this study, nine methods for measuring indirect importance are compared on the basis of their discriminatory power and stability. To the best knowledge of the authors, the stability of results obtained with different methods is assessed for the first time. The deficiencies of an existing criterion for assessing diagnosticity are pointed out and a modified version suggested. The empirical comparison is based on two real-world datasets from the ecommerce industry. Even though none of the methods appeared to be the best according to both criteria simultaneously, there seem to be grounds for recommending the theoretically sound Shapley value decomposition of R-square if stability and discrimination are about equally important for a decision maker, while negative contributions are undesirable.

PRODUCT DEVELOPMENT CAPABILITIES: EXPLORING MEDIATING AND MODERATING LINKS BETWEEN INTERNATIONAL PRODUCT ADAPTATION AND INDUSTRIAL MANUFACTURERS’ BUSINESS PERFORMANCE

2020 ◽  
Vol 24 (07) ◽  
pp. 2050084
Author(s):  
KATHARINA BAUER ◽  
STEPHANIE KRINNER ◽  
ROLAND HELM ◽  
KATHARINA RAABE
Keyword(s):  
Product Development ◽  
Marketing Strategy ◽  
Business Performance ◽  
Marketing Capabilities ◽  
Marketing Capability ◽  
Information Display ◽  
Strong Product ◽  
And Performance ◽  
First Time ◽  
Development Capability

Product development is the crucial marketing capability for successfully and sustainably bringing new or adapted products to the market. Throughout the process, companies face challenges in how to adapt their products to international unknown markets while simultaneously enhancing their business performance. Inconsistent and confusing results on this relationship dominate previous research. We argue that the realisation of the marketing strategy of international product adaptation can only be accomplished by relying on a firm’s product development capability. The mediating link between product adaptation and performance induced by the product development capability moderated by sales integration and information processing was examined and confirmed for industrial goods manufacturers for the first time, which advances marketing capabilities theory in approving that the implementation of marketing mix capabilities into marketing strategy, the integration of the sales function and the processing of market information display a performance augmenting effect. Our results show that firms have to evolve a strong product development capability in order to be able to successfully implement strategic international adaptation decisions.

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