Multi-stage ranking pipelines have been a practical solution in modern search systems, where the first-stage retrieval is to return a subset of candidate documents, and the latter stages attempt to re-rank those candidates. Unlike the re-ranking stages going through quick technique shifts during the past decades, the first-stage retrieval has long been dominated by classical term-based models. Unfortunately, these models suffer from the vocabulary mismatch problem, which may block the re-ranking stages from relevant documents at the very beginning. Therefore, it has been a long-term desire to build semantic models for the first-stage retrieval that can achieve high recall efficiently. Recently, we have witnessed an explosive growth of research interests on the first-stage semantic retrieval models. We believe it is the right time to survey the current status, learn from existing methods, and gain some insights for future development. In this paper, we describe the current landscape of semantic retrieval models from three major paradigms, paying special attention to recent neural-based methods. We review the benchmark datasets, optimization methods and evaluation metrics, and summarize the state-of-the-art models. We also discuss the unresolved challenges and suggest potentially promising directions for future work.

Semantic Models for the First-stage Retrieval: A Comprehensive Review.

A CBR framework with gradient boosting based feature selection for lung cancer subtype classification.

The computational workload is one of the key challenges in biometric identification systems. The naive retrieval method based on an exhaustive search becomes impractical with the growth of the number of the enrolled data subjects. Consequently, in recent years, many methods with the aim of reducing or optimising the computational workload, and thereby speeding-up the identification transactions, in biometric identification systems have been developed. In this article, taxonomy for conceptual categorisation of such methods is presented, followed by a comprehensive survey of the relevant academic publications, including computational workload reduction and software/hardware-based acceleration. Lastly, the pertinent technical considerations and trade-offs of the surveyed methods are discussed, along with an industry perspective, and open issues/challenges in the field.

Computational workload in biometric identification systems: an overview

The main purpose of feature subset selection is to remove irrelevant and redundant features from data, so that learning algorithms can be trained by a subset of relevant features. So far, many algorithms have been developed for the feature subset selection, and most of these algorithms suffer from two major problems in solving high-dimensional datasets: First, some of these algorithms search in a high-dimensional feature space without any domain knowledge about the feature importance. Second, most of these algorithms are originally designed for continuous optimization problems, but feature selection is a binary optimization problem. To overcome the mentioned weaknesses, we propose a novel hybrid filter-wrapper algorithm, called Ensemble of Filter-based Rankers to guide an Epsilon-greedy Swarm Optimizer (EFR-ESO), for solving high-dimensional feature subset selection. The Epsilon-greedy Swarm Optimizer (ESO) is a novel binary swarm intelligence algorithm introduced in this paper as a novel wrapper. In the proposed EFR-ESO, we extract the knowledge about the feature importance by the ensemble of filter-based rankers and then use this knowledge to weight the feature probabilities in the ESO. Experiments on 14 high-dimensional datasets indicate that the proposed algorithm has excellent performance in terms of both the error rate of the classification and minimizing the number of features.

Ensemble of Filter-Based Rankers to Guide an Epsilon-Greedy Swarm Optimizer for High-Dimensional Feature Subset Selection

Network alignment, in general, seeks to discover the hidden underlying correspondence between nodes across two (or more) networks when given their network structure. However, most existing network alignment methods have added assumptions of additional constraints to guide the alignment, such as having a set of seed node-node correspondences across the networks or the existence of side-information. Instead, we seek to develop a general network alignment algorithm that makes no additional assumptions. Recently, network embedding has proven effective in many network analysis tasks, but embeddings of different networks are not aligned. Thus, we present our Deep Adversarial Network Alignment (DANA) framework that first uses deep adversarial learning to discover complex mappings for aligning the embedding distributions of the two networks. Then, using our learned mapping functions, DANA performs an efficient nearest neighbor node alignment. We perform experiments on real world datasets to show the effectiveness of our framework for first aligning the graph embedding distributions and then discovering node alignments that outperform existing methods.

Deep Adversarial Network Alignment

Nowadays, the need to techniques, approaches, and algorithms to search on data is increased due to improvements in computer science and increasing amount of information. This ever increasing information volume has led to time and computation complexity. Recently, different methods to solve such problems are proposed. Among the others, nearest neighbor search is one of the best techniques to this end which is focused by many researchers. Different techniques are used for nearest neighbor search. In addition to put an end to some complexities, variety of these techniques has made them suitable for different applications such as pattern recognition, searching in multimedia data, information retrieval, databases, data mining, and computational geometry to name but a few. In this paper, by opening a new view to this problem, a comprehensive evaluation on structures, techniques and different algorithms in this field is done and a new categorization of techniques in NNS is presented. This categorization is consists of seven groups: Weighted, Reductional, Additive, Reverse, Continuous, Principal Axis and Other techniques which are studied, evaluated and compared in this paper. Complexity of used structures, techniques and their algorithms are discussed, as well.

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Mohammad RezaAbbasifard

Papers

A Survey on Nearest Neighbor Search Methods