Top-Down Induction of Similarity Measures Using Similarity Clouds

Gabel, Thomas; Godehardt, Eicke

doi:10.1007/978-3-319-24586-7_11

Cited by 13 publications

(15 citation statements)

References 14 publications

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“…Gabel and Godehardt [10] used a neural network to learn a similarity measure. Their work is done in the context of casebased reasoning (CBR) which uses the measure to retrieve similar cases.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, we implemented a Type 2 similarity measure gabel as described by Gabel et al [10]. The architecture of gabel is shown in Fig.…”

Section: Reference Similarity Measuresmentioning

confidence: 99%

“…It is worth mentioning that this dataset is of size N (N − 1) for the similarity measure to train on all possible combinations of the N data points. Certain similarity measure architectures (e.g., gabel from Gabel et al [10] or Zagoruyko et al's similarity measures [33] ) need to train on a dataset containing all possible combinations of data points (of size N (N −1)) as training on the triplet (x, y, s) does not guarantee that the model learns that S( y, x) = s. Thus, the training dataset must also include the triplet ( y, x, s). However, this may be largely avoided by using architectures (such as those seen in SNNs and SNs) that exploit symmetry and weight sharing.…”

Section: Type 4 Similarity Measurementioning

confidence: 99%

“…How to automatically learn similarity measures has been an active area of research in CBR. For instance, Gabel et al [10] trained a similarity measure by creating a dataset of Fig. 1 Illustration of problem and solution spaces [19].…”

Section: Introductionmentioning

confidence: 99%

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Learning similarity measures from data

et al. 2019

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Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or a set of cases most similar to the query case. Describing a similarity measure analytically is challenging, even for domain experts working with CBR experts. However, datasets are typically gathered as part of constructing a CBR or machine learning system. These datasets are assumed to contain the features that correctly identify the solution from the problem features; thus, they may also contain the knowledge to construct or learn such a similarity measure. The main motivation for this work is to automate the construction of similarity measures using machine learning. Additionally, we would like to do this while keeping training time as low as possible. Working toward this, our objective is to investigate how to apply machine learning to effectively learn a similarity measure. Such a learned similarity measure could be used for CBR systems, but also for clustering data in semi-supervised learning, or one-shot learning tasks. Recent work has advanced toward this goal which relies on either very long training times or manually modeling parts of the similarity measure. We created a framework to help us analyze the current methods for learning similarity measures. This analysis resulted in two novel similarity measure designs: The first design uses a pre-trained classifier as basis for a similarity measure, and the second design uses as little modeling as possible while learning the similarity measure from data and keeping training time low. Both similarity measures were evaluated on 14 different datasets. The evaluation shows that using a classifier as basis for a similarity measure gives state-of-the-art performance. Finally, the evaluation shows that our fully data-driven similarity measure design outperforms state-of-the-art methods while keeping training time low.

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Section: Related Workmentioning

confidence: 99%

“…Furthermore, we implemented a Type 2 similarity measure gabel as described by Gabel et al [10]. The architecture of gabel is shown in Fig.…”

Section: Reference Similarity Measuresmentioning

confidence: 99%

Section: Type 4 Similarity Measurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning similarity measures from data

et al. 2019

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“…Therewith we can tailor each similarity measure to the application domain. Using a data-driven approach for automatic similarity learning and feature weighting has been presented by Gabel and Godehardt [3] where they trained a neural network to induce local and global similarity measures [5]. While we are not automatically assigning the similarity measures, we use the existing cases to derive them.…”

Section: Related Workmentioning

confidence: 99%

Similarity Measure Development for Case-Based Reasoning–A Data-Driven Approach

Verma

Bach

Mork

2019

Communications in Computer and Information Science

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In this paper, we demonstrate a data-driven methodology for modelling the local similarity measures of various attributes in a dataset. We analyse the spread in the numerical attributes and estimate their distribution using polynomial function to showcase an approach for deriving strong initial value ranges of numerical attributes and use a non-overlapping distribution for categorical attributes such that the entire similarity range [0,1] is utilized. We use an open source dataset for demonstrating modelling and development of the similarity measures and will present a case-based reasoning (CBR) system that can be used to search for the most relevant similar cases.

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Modelling Similarity for Comparing Physical Activity Profiles - A Data-Driven Approach

Verma

Bach

Mork

2018

Case-Based Reasoning Research and Development

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Objective measurements of physical behaviour are an interesting research field from the public health and computer science perspective. While for public health research, measurements with a high quality and feasible setup is important, the analysis of and reasoning about the data is what we will present in this work. Our focus in this work is the comprehensive representation of physical behaviour throughout consecutive days and allowing to find subgroups in the population with similar physical activity levels. We have a unique data set of 4628 participants wearing tri-axial accelerometers for six days and will present a case-based reasoning (CBR) system that can find and compare similar activity profiles. In this work, we focus on creating a CBR model using myCBR and do initial experiments with the resulting system. We will introduce a data-driven approach for modelling local similarity measures. Eventually, in the experiments we will show that for the given data set, the CBR system outperforms a k-Nearest Neighbor regressor in finding most similar participants.

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Top-Down Induction of Similarity Measures Using Similarity Clouds

Cited by 13 publications

References 14 publications

Learning similarity measures from data

Learning similarity measures from data

Similarity Measure Development for Case-Based Reasoning–A Data-Driven Approach

Modelling Similarity for Comparing Physical Activity Profiles - A Data-Driven Approach

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