Unsupervised Matching Research Articles

Pre-Trained Embeddings for Entity Resolution: An Experimental Analysis

Many recent works on Entity Resolution (ER) leverage Deep Learning techniques involving language models to improve effectiveness. This is applied to both main steps of ER, i.e., blocking and matching. Several pre-trained embeddings have been tested, with the most popular ones being fastText and variants of the BERT model. However, there is no detailed analysis of their pros and cons. To cover this gap, we perform a thorough experimental analysis of 12 popular language models over 17 established benchmark datasets. First, we assess their vectorization overhead for converting all input entities into dense embeddings vectors. Second, we investigate their blocking performance, performing a detailed scalability analysis, and comparing them with the state-of-the-art deep learning-based blocking method. Third, we conclude with their relative performance for both supervised and unsupervised matching. Our experimental results provide novel insights into the strengths and weaknesses of the main language models, facilitating researchers and practitioners to select the most suitable ones in practice.

Unsupervised SAR and Optical Image Matching Using Siamese Domain Adaptation

Due to its highly complementary information about remote sensing, synthetic aperture radar (SAR) and optical imagery matching have drawn much attention in recent years. Compared with traditional methods, deep learning-based SAR-optical image matching models largely rely on supervision with ground truths, where the matching accuracy suffers because of unseen image domains. To mitigate loads in burdensome labeling tasks, transferring deep learning models trained with annotated source domains to nonannotated target domains has attracted great concern. Due to the domain gap, the difference between the source and target domains is likely to deteriorate the matching accuracy on target data if the training process is directly conducted without proper domain adaptation (DA). In this research, a Siamese DA (SDA) approach with a combined loss function is developed in the context of multimodality image matching. Then, a novel rotation/scale-invariant transformation module with regression modules is designed to extract rotation/scale-equivariant features. Finally, the causal inference-based self-learning method and the multiresolution histogram matching approach are employed to enhance the unsupervised matching performance. Experimental results on the RadarSat/Planet dataset and the Sentinel-1/2 dataset demonstrate that the developed model can achieve competitive matching performance with a low overlap ratio between domains and little data labeling. By alleviating the domain discrepancy, the developed model drastically reduces the average L2 score of the unsupervised matching from 9.576 to 0.658, while the less-than-one-pixel matching error rate is enhanced from 66.3% to 90.6%.

Self-reinforcing Unsupervised Matching

Remarkable gains in deep learning usually benefit from large-scale supervised data. Ensuring the intra-class modality diversity in training set is critical for generalization capability of cutting-edge deep models, but it burdens human with heavy manual labor on data collection and annotation. In addition, some rare or unexpected modalities are new for the current model, causing reduced performance under such emerging modalities. Inspired by the achievements in speech recognition, psychology and behavioristics, we present a practical solution, self-reinforcing unsupervised matching (SUM), to annotate the images with 2D structure-preserving property in an emerging modality by cross-modality matching. Specifically, we propose a dynamic programming algorithm, dynamic position warping (DPW), to reveal the underlying element correspondence relationship between two matrix-form data in an order-preserving fashion, and devise a local feature adapter (LoFA) to allow for cross-modality similarity measurement. On these bases, we develop a two-tier self-reinforcing learning mechanism on both feature level and image level to optimize the LoFA. The proposed SUM framework requires no any supervision in emerging modality and only one template in seen modality, providing a promising route towards incremental learning and continual learning. Extensive experimental evaluation on two proposed challenging one-template visual matching tasks demonstrate its efficiency and superiority.

Gum-Net: Unsupervised Geometric Matching for Fast and Accurate 3D Subtomogram Image Alignment and Averaging.

We propose a Geometric unsupervised matching Network (Gum-Net) for finding the geometric correspondence between two images with application to 3D subtomogram alignment and averaging. Subtomogram alignment is the most important task in cryo-electron tomography (cryo-ET), a revolutionary 3D imaging technique for visualizing the molecular organization of unperturbed cellular landscapes in single cells. However, subtomogram alignment and averaging are very challenging due to severe imaging limits such as noise and missing wedge effects. We introduce an end-to-end trainable architecture with three novel modules specifically designed for preserving feature spatial information and propagating feature matching information. The training is performed in a fully unsupervised fashion to optimize a matching metric. No ground truth transformation information nor category-level or instance-level matching supervision information is needed. After systematic assessments on six real and nine simulated datasets, we demonstrate that Gum-Net reduced the alignment error by 40 to 50% and improved the averaging resolution by 10%. Gum-Net also achieved 70 to 110 times speedup in practice with GPU acceleration compared to state-of-the-art subtomogram alignment methods. Our work is the first 3D unsupervised geometric matching method for images of strong transformation variation and high noise level. The training code, trained model, and datasets are available in our open-source software AITom.

A self-verifying clustering approach to unsupervised matching of product titles

The continuous growth of the e-commerce industry has rendered the problem of product retrieval particularly important. As more enterprises move their activities on the Web, the volume and the diversity of the product-related information increase quickly. These factors make it difficult for the users to identify and compare the features of their desired products. Recent studies proved that the standard similarity metrics cannot effectively identify identical products, since similar titles often refer to different products and vice-versa. Other studies employ external data sources to enrich the titles; these solutions are rather impractical, since the process of fetching external data is inefficient. In this paper we introduce UPM, an unsupervised algorithm for matching products by their titles that is independent of any external sources. UPM consists of three stages. During the first stage, the algorithm analyzes the titles and extracts combinations of words out of them. These combinations are evaluated in stage 2 according to several criteria, and the most appropriate of them are selected to form the initial clusters. The third phase is a post-processing verification stage that refines the initial clusters by correcting the erroneous matches. This stage is designed to operate in combination with all clustering approaches, especially when the data possess properties that prevent the co-existence of two data points within the same cluster. The experimental evaluation of UPM with multiple datasets demonstrates its superiority against the state-of-the-art clustering approaches and string similarity metrics, in terms of both efficiency and effectiveness.

A Stochastic Hyperheuristic for Unsupervised Matching of Partial Information

This paper (Revised version of a white paper “Unsupervised Problem-Solving by Optimising through Comparisons,” originally published on DCS and Scribd, October 2011.) describes the implementation and functionality of a centralised problem solving system that is included as part of the distributed “licas” system. This is an open source framework for building service-based networks, similar to what you would do on a Cloud or SOA platform. While the framework can include autonomous and distributed behaviour, the problem-solving part can perform more complex centralised optimisation operations and then feed the results back into the network. The problem-solving system is based on a novel type of evaluation mechanism that prefers comparisons between solution results, over maximisation. This paper describes the advantages of that and gives some examples of where it might perform better, including possibilities related to a more cognitive system.

HAMSTER

We address the problem of unsupervised matching of schema information from a large number of data sources into the schema of a data warehouse. The matching process is the first step of a framework to integrate data feeds from third-party data providers into a structured-search engine's data warehouse. Our experiments show that traditional schema-based and instance-based schema matching methods fall short. We propose a new technique based on the search engine's clicklogs. Two schema elements are matched if the distribution of keyword queries that cause click-throughs on their instances are similar. We present experiments on large commercial datasets that show the new technique has much better accuracy than traditional techniques.

Spatial color image retrieval without segmentation using thumbnails and the Earth Mover's Distance

We introduce a spatial color image retrieval method which does not include any segmentation step. This method relies on small image thumbnails and the Earth Mover's Distance (EMD). We then derive an unsupervised matching criterion using an a contrario approach. Experiments are performed on a database of illuminated manuscripts.