Simple Similarity Measure Research Articles

ILFDA Model: An Online Soft Measurement Method Using Improved Local Fisher Discriminant Analysis

With the advancement of soft measurement, just-in-time learning (JITL) has become a widely adopted framework for online soft-sensing modeling in industrial processes. However, traditional JITL model approaches often rely on simple similarity measures like Euclidean distance, resulting in the underutilization of labeled data. This paper proposes a supervised, improved local Fisher discriminant analysis method based on a JITL framework and local Fisher discriminant analysis (LFDA) to improve data utilization efficiency. In particular, by incorporating the indirect correlation information matrix, this method integrates the inter-class and intra-class dispersion matrix, overcoming the limitation of the LFDA algorithm that only captures direct data correlations. We select two different carbon depositions in the Methanol-to-Olefin reaction system for comparative experiments and use the root mean squared error (RMSE) and R-square (R2) to evaluate the effectiveness of the proposed method. Fitting results show that two kinds of carbon depositions were better than the control model, namely the RMSE of the model were 0.1431 and 0.1513, R2 were 0.8952 and 0.8707.

Predicted as observed? How to identify empirically adequate theoretical constructs.

The identification of an empirically adequate theoretical construct requires determining whether a theoretically predicted effect is sufficiently similar to an observed effect. To this end, we propose a simple similarity measure, describe its application in different research designs, and use computer simulations to estimate the necessary sample size for a given observed effect. As our main example, we apply this measure to recent meta-analytical research on precognition. Results suggest that the evidential basis is too weak for a predicted precognition effect of d = 0.20 to be considered empirically adequate. As additional examples, we apply this measure to object-level experimental data from dissonance theory and a recent crowdsourcing hypothesis test, as well as to meta-analytical data on the correlation of personality traits and life outcomes.

MDESNet: Multitask Difference-Enhanced Siamese Network for Building Change Detection in High-Resolution Remote Sensing Images

Building change detection is a prominent topic in remote sensing applications. Scholars have proposed a variety of fully-convolutional-network-based change detection methods for high-resolution remote sensing images, achieving impressive results on several building datasets. However, existing methods cannot solve the problem of pseudo-changes caused by factors such as “same object with different spectrums” and “different objects with same spectrums” in high-resolution remote sensing images because their networks are constructed using simple similarity measures. To increase the ability of the model to resist pseudo-changes and improve detection accuracy, we propose an improved method based on fully convolutional network, called multitask difference-enhanced Siamese network (MDESNet) for building change detection in high-resolution remote sensing images. We improved its feature extraction ability by adding semantic constraints and effectively utilized features while improving its recognition performance. Furthermore, we proposed a similarity measure combining concatenation and difference, called the feature difference enhancement (FDE) module, and designed comparative experiments to demonstrate its effectiveness in resisting pseudo-changes. Using the building change detection dataset (BCDD), we demonstrate that our method outperforms other state-of-the-art change detection methods, achieving the highest F1-score (0.9124) and OA (0.9874), indicating its advantages for high-resolution remote sensing image building change detection tasks.

Registration of Multisensor Images through a Conditional Generative Adversarial Network and a Correlation-Type Similarity Measure

The automatic registration of multisensor remote sensing images is a highly challenging task due to the inherently different physical, statistical, and textural characteristics of the input data. Information-theoretic measures are often used to favor comparing local intensity distributions in the images. In this paper, a novel method based on the combination of a deep learning architecture and a correlation-type area-based functional is proposed for the registration of a multisensor pair of images, including an optical image and a synthetic aperture radar (SAR) image. The method makes use of a conditional generative adversarial network (cGAN) in order to address image-to-image translation across the optical and SAR data sources. Then, once the optical and SAR data are brought to a common domain, an area-based ℓ2 similarity measure is used together with the COBYLA constrained maximization algorithm for registration purposes. While correlation-type functionals are usually ineffective in the application to multisensor registration, exploiting the image-to-image translation capabilities of cGAN architectures allows moving the complexity of the comparison to the domain adaptation step, thus enabling the use of a simple ℓ2 similarity measure, favoring high computational efficiency, and opening the possibility to process a large amount of data at runtime. Experiments with multispectral and panchromatic optical data combined with SAR images suggest the effectiveness of this strategy and the capability of the proposed method to achieve more accurate registration as compared to state-of-the-art approaches.

Virtual information core optimization for collaborative filtering recommendation based on clustering and evolutionary algorithms

Collaborative filtering (CF), the most widely used recommendation algorithm, has to face the sparsity and scalability problem. Some researchers proposed to select a representative set of real users called information core (IC) from all the real users, which is used as the candidate neighbor set in the CF to alleviate the scalability problem. However, the rating vectors of these real users that compose IC are usually sparse, which will negatively affect the recommendation accuracy. In this paper, a virtual information core (VIC) optimization algorithm is proposed based on clustering and evolutionary algorithms for CF recommendation (VICO-CEA). The problem of searching for VIC is modeled as a combinatorial optimization problem, and is solved offline by the proposed evolutionary algorithm. The VIC is a set of virtual core users, each of which is constructed by averaging out multiple real users. These virtual core users in the VIC are no longer sparse and are found by the evolutionary optimization, which will improve the recommendation accuracy and reduce the online recommendation time as the VIC is used as the candidate neighbor set in the CF. Meanwhile, to make offline optimization more efficient, two strategies are proposed. One is to design a simple similarity measure based on dimensionality reduction and clustering to save time in calculating similarities by reducing the dimensionality of users’ rating vectors. The other is to use dimensionality reduction and clustering to construct a smaller training set and validation set by reducing the dimensionality of items’ rating vectors. The experimental results show that VICO-CEA can not only significantly reduce the online recommendation time further but also improve the recommendation accuracy greatly compared to traditional CF and other information-core-based methods.

Clustering of Voivodships in Poland According to the Effectiveness of Professional Activisation in the Aspect of Changes in Procedures

Purpose: The aim of the research is the assessment of voivodships according to the effectiveness of basic forms of professional activisation and comparison of the results obtained for the new and old procedures of their determination. Design/Methodology/Approach: The voivodships were clustered by using the k-means method for both new and old procedures of their determination. The impact of changes introduced from 2015 on results of clustering. The differences were examined using a simple measure of similarity. The study was conducted on the basis of statistical data on cost and employment effectiveness published in the Ministry of Family, Labour and Social Policy. Findings: The greatest effectiveness of the use of the funds allocated for economic activisation was observed in Pomorskie, Lubuskie and Opolskie Voivodships, and the smallest in Mazowieckie, Warminsko-Mazurskie, Świetokrzyskie and Podkarpackie Voivodships. The voivodships, in which greater funds were allocated for activisation (there were high values of cost effectiveness), generally used them to a lesser extent (employment effectiveness values were lower) and vice versa. In the analysed period, a change in the procedure of determination of effectiveness measures generally did not cause large differences in results of clustering. The year 2016 was an exception, and in 2018 the results of clustering were identical. Practical Implications: The presented research can be used to assess the impact of various factors on socio-economic phenomena. It makes it possible to determine the impact of regulatory changes on the effectiveness of various social policy instruments. Originality/value: The added value of the research is the comparison of results for both procedures of determination of effectiveness measures.

SOM-based binary coding for single sample face recognition

Due to the semantic gap between the insufficient facial features and facial identifying information, the single sample per person (SSPP) problem has always been a significant challenge in the field of facial recognition. To address this problem, this paper proposes a Self-Organizing Map (SOM)-based binary coding (SOM-BC) method, which extracts the middle-level semantic features by merging the SOM network with the Bag-of-Features (BoF) model. First, we extract the local features of the facial images using the SIFT descriptor. Next, inspired by human visual perception, we utilize a SOM neural network to obtain a visual words dictionary capable of reflecting the intrinsic structure of facial features in semantic space. Subsequently, a binary coding method is further proposed to map the local features into semantic space. Finally, we propose a simple but effective similarity measure method for classification. Experimental results on three public databases not only demonstrate the effectiveness of the proposed method, but also its high computational efficiency.

GAN–SOM: A clustering framework with SOM-similar network based on deep learning

Since an increasing amount of data is generated and collected in real life, clustering is more frequently applied to process these unlabeled data in practical problems. Due to the simple similarity measure of conventional clustering methods, they are unable to achieve good performance on current big data. With the popularity of deep learning, deep clustering has been developed in recent years and obtained remarkable results. However, they have complex architecture and consume numerous computational resources, which goes against the migration to edge devices. Therefore, methods with low cost are required to satisfy edge computing, which is the trend of development. In this paper, we propose GAN–SOM as a new architecture for clustering based on deep learning. A SOM-similar network is designed to simultaneously implement encoding and clustering purposes on data samples, which is jointly trained with a GAN to optimizes a new defined clustering loss. We also utilize self-attention mechanism and spectral normalization in the GAN architecture to enhance effects of generated data, which aims to achieve better clustering results. The experimental results compared with other clustering baselines with deep learning verify that our method maintains high clustering metrics while saving computational cost significantly.

Deep Learning Driven GC-MS Library Search and Its Application for Metabolomics

Preliminary compound identification and peak annotation in gas chromatography-mass spectrometry is usually made using mass spectral databases. There are a few algorithms that enable performing a search of a spectrum in a large mass spectral library. In many cases, a library search procedure returns a wrong answer even if a correct compound is contained in a library. In this work, we present a deep learning driven approach to a library search in order to reduce the probability of such cases. Machine learning ranking (learning to rank) is a class of machine learning and deep learning algorithms that perform a comparison (ranking) of objects. This work introduces the usage of deep learning ranking for small molecules identification using low-resolution electron ionization mass spectrometry. Instead of simple similarity measures for two spectra, such as the dot product or the Euclidean distance between vectors that represent spectra, a deep convolutional neural network is used. The deep learning ranking model outperforms other approaches and enables reducing a fraction of wrong answers (at rank-1) by 9-23% depending on the used data set. Spectra from the Golm Metabolome Database, Human Metabolome Database, and FiehnLib were used for testing the model.

A comparative analysis of similarity measures akin to the Jaccard index in collaborative recommendations: empirical and theoretical perspective

Jaccard index, originally proposed by Jaccard (Bull Soc Vaudoise Sci Nat 37:241–272, 1901), is a measure for examining the similarity (or dissimilarity) between two sample data objects. It is defined as the proportion of the intersection size to the union size of the two data samples. It provides a very simple and intuitive measure of similarity between data samples. This research examines the measures that are akin to the Jaccard index and may be used for modelling affinity between users (or items) in collaborative recommendations. Particularly, the measures such as simple matching coefficient (SMC), Sorensen–Dice coefficient (SDC), Salton’s cosine index (SCI), and overlap coefficient (OLC) are compared and analysed in both theoretical and empirical perspectives with respect to the Jaccard index. Since these measures apprehend only the structural similarity information (overlapping information) between the data samples, these are very useful in situations where only the associations between users and items are available such as browsing or buying behaviours of the users on an e-commerce portal (i.e. unary rating data, a special case of ratings). Furthermore, a theoretical relation among these measures has been established. We have also derived an equivalent expression for each of these measures so that it can be directly applied for binary data samples in data mining/machine learning jargon. In order to compare and validate the effectiveness of these structural similarity measures, several experiments have been conducted using standardized benchmark datasets (MovieLens, FilmTrust, Epinions, Yahoo! Movies, and Yahoo! Music). Empirically obtained results demonstrate that the Salton’s cosine index (SCI) provides better accuracy (in terms of MAE, RMSE, and precision) for large datasets, whereas the overlap coefficient (OLC) results in more accurate recommendations for small datasets.

Finding Diagnostically Useful Patterns in Quantitative Phenotypic Data

Trio-based whole-exome sequence (WES) data have established confident genetic diagnoses in ∼40% of previously undiagnosed individuals recruited to the Deciphering Developmental Disorders (DDD) study. Here we aim to use the breadth of phenotypic information recorded in DDD to augment diagnosis and disease variant discovery in probands. Median Euclidean distances (mEuD) were employed as a simple measure of similarity of quantitative phenotypic data within sets of ≥10 individuals with plausibly causative de novo mutations (DNM) in 28 different developmental disorder genes. 13/28 (46.4%) showed significant similarity for growth or developmental milestone metrics, 10/28 (35.7%) showed similarity in HPO term usage, and 12/28 (43%) showed no phenotypic similarity. Pairwise comparisons of individuals with high-impact inherited variants to the 32 individuals with causative DNM in ANKRD11 using only growth z-scores highlighted 5 likely causative inherited variants and two unrecognized DNM resulting in an 18% diagnostic uplift for this gene. Using an independent approach, naive Bayes classification of growth and developmental data produced reasonably discriminative models for the 24 DNM genes with sufficiently complete data. An unsupervised naive Bayes classification of 6,993 probands with WES data and sufficient phenotypic information defined 23 in silico syndromes (ISSs) and was used to test a “phenotype first” approach to the discovery of causative genotypes using WES variants strictly filtered on allele frequency, mutation consequence, and evidence of constraint in humans. This highlighted heterozygous de novo nonsynonymous variants in SPTBN2 as causative in three DDD probands.

A new similarity measure for collaborative filtering based recommender systems

The objective of a recommender system is to provide customers with personalized recommendations while selecting an item among a set of products (movies, books, etc.). The collaborative filtering is the most used technique for recommender systems. One of the main components of a recommender system based on the collaborative filtering technique, is the similarity measure used to determine the set of users having the same behavior with regard to the selected items. Several similarity functions have been proposed, with different performances in terms of accuracy and quality of recommendations. In this paper, we propose a new simple and efficient similarity measure. Its mathematical expression is determined through the following paper contributions: 1) transforming some intuitive and qualitative conditions, that should be satisfied by the similarity measure, into relevant mathematical equations namely: the integral equation, the linear system of differential equations and a non-linear system and 2) resolving the equations to achieve the kernel function of the similarity measure. The extensive experimental study driven on a benchmark datasets shows that the proposed similarity measure is very competitive, especially in terms of accuracy, with regards to some representative similarity measures of the literature.

An automatic and serialized ROI extraction framework for the slow-motion video frames

Currently, high-speed cameras has been a very common equipment in many important application fields. How to effectively and automatically extract the ROI (region of interest) for the slow-motion video has been a novel interesting challenge. In recent research work, we designed a ROI extraction framework for the video frames produced by high-speed cameras. The entire framework includes two parts: a novel but simple color similarity measure model is improved to distinguish different pixels; a skeleton feature points based serialized segmentation tactics is proposed to generate seed points. By using the multithreading patterns of parallelizing computations in the extraction process, the ROI in the serialized color slow-motion video frames can be marked automatically and accurately. Comparing with the common methods, this method has advantage in segmentation effect and computational efficiency. It can establish the technical basis for the pertinent subsequent studies.

Contextual case-based reasoning applied to a mobile device

PurposeThis paper aims to apply a contextual case-based reasoning (CBR) to a mobile device. The CBR method was chosen because it does not require training, demands minimal processing resources and easily integrates with the dynamic and uncertain nature of pervasive computing. Based on a mobile user’s location and activity, which can be determined through the device’s inertial sensors and GPS capabilities, it is possible to select and offer appropriate services to this user.Design/methodology/approachThe proposed approach comprises two stages. The first stage uses simple semantic similarity measures to retrieve the case from the case base that best matches the current case. In the second stage, the obtained selection of services is then filtered based on current contextual information.FindingsThis two-stage method adds a higher level of relevance to the services proposed to the user; yet, it is easy to implement on a mobile device.Originality/valueA two-stage CBR using light processing methods and generating context aware services is discussed. Ontological location modeling adds reasoning flexibility and knowledge sharing capabilities.

Multimodal image registration based on binary gradient angle descriptor.

Multimodal image registration plays an important role in image-guided interventions/therapy and atlas building, and it is still a challenging task due to the complex intensity variations in different modalities. The paper addresses the problem and proposes a simple, compact, fast and generally applicable modality-independent binary gradient angle descriptor (BGA) based on the rationale of gradient orientation alignment. The BGA can be easily calculated at each voxel by coding the quadrant in which a local gradient vector falls, and it has an extremely low computational complexity, requiring only three convolutions, two multiplication operations and two comparison operations. Meanwhile, the binarized encoding of the gradient orientation makes the BGA more resistant to image degradations compared with conventional gradient orientation methods. The BGA can extract similar feature descriptors for different modalities and enable the use of simple similarity measures, which makes it applicable within a wide range of optimization frameworks. The results for pairwise multimodal and monomodal registrations between various images (T1, T2, PD, T1c, Flair) consistently show that the BGA significantly outperforms localized mutual information. The experimental results also confirm that the BGA can be a reliable alternative to the sum of absolute difference in monomodal image registration. The BGA can also achieve an accuracy of [Formula: see text], similar to that of the SSC, for the deformable registration of inhale and exhale CT scans. Specifically, for the highly challenging deformable registration of preoperative MRI and 3D intraoperative ultrasound images, the BGA achieves a similar registration accuracy of [Formula: see text] compared with state-of-the-art approaches, with a computation time of 18.3s per case. The BGA improves the registration performance in terms of both accuracy and time efficiency. With further acceleration, the framework has the potential for application in time-sensitive clinical environments, such as for preoperative MRI and intraoperative US image registration for image-guided intervention.

Spatio-semantic user profiles in location-based social networks

Knowledge of users’ visits to places is one of the keys to understanding their interest in places. User-contributed annotations of place, the types of places they visit, and the activities they carry out, add a layer of important semantics that, if considered, can result in more refined representations of user profiles. In this paper, semantic information is summarised as tags for places and a folksonomy data model is used to represent spatial and semantic relationships between users, places, and tags. The model allows simple co-occurrence methods and similarity measures to be applied to build different views of personalised user profiles. Basic profiles capture direct user interactions, while enriched profiles offer an extended view of users’ association with places and tags that take into account relationships in the folksonomy. The main contributions of this work are the proposal of a uniform approach to the creation of user profiles on the Social Web that integrates both the spatial and semantic components of user-provided information, and the demonstration of the effectiveness of this approach with realistic datasets.

A scalable framework for NBA player and team comparisons using player tracking data

The release of NBA player tracking data greatly enhances the granularity and dimensionality of basketball statistics used to evaluate and compare player performance. However, the high dimensionality of this new data source can be troublesome as it demands more computational resources and reduces the ability to easily interpret findings. Therefore, we must find a way to reduce the dimensionality of the data while retaining the ability to differentiate and compare player performance. In this paper, Principal Component Analysis (PCA) is used to identify four principal components that account for 68% of the variation in player tracking data from the 2013-2014 regular season and intuitive interpretations of these new dimensions are developed by examining the statistics that influence them the most. In this new high variance, low dimensional space, you can easily compare statistical profiles across any or all of the principal component dimensions to evaluate characteristics that make certain players and teams similar or unique. A simple measure of similarity between two player or team statistical profiles based on the four principal component scores is also constructed. The Statistical Diversity Index (SDI) allows for quick and intuitive comparisons using the entirety of the player tracking data. As new statistics emerge, this framework is scalable as it can incorporate existing and new data sources by reconstructing the principal component dimensions and SDI for improved comparisons. Using principal component scores and SDI, several use cases are presented for improved personnel management.

Improved similarity measure in neutrosophic environment and its application in finding minimum spanning tree

Minimum spanning tree finds its huge application in network designing, approximation algorithms for NP-hard problems, clustering problems and many more. Many research works have been done to find minimum spanning tree due to its various applications. But, till date very few research works are available in finding minimum spanning tree in neutrosophic environment. This paper contributes significantly by defining the weight of each network edge using single valued neutro- sophic set (SVNS) and introduce a new approach using similarity measure to find minimum spanning tree in neutrosophic environment. Use of SVNS makes the problem realistic as it can describe the uncertainty, indeterminacy and hesitancy of the real world in a better way. We introduce two new and simple similarity measures to overcome some disadvantages of existing Jaccard, Dice and Cosine similarity measures of SVNSs for ranking the alternatives. Further from the similarity measures we have developed two formulas for the entropy measure proving a fundamental relation between similarity measure and entropy measure. The new entropy measures define the uncertainty more explicitly in comparison to other entropy measure existing in the literature which has been established using an example.

Quantitative Evaluation of Web user Session Dissimilarity measures using Medoids based Relational Fuzzy clustering

Background/Objectives: Proficient relational clustering of web users’ sessions not only depends on clustering algorithm’s character but also profoundly influenced by the used dissimilarity measures. Therefore, determining the right dissimilarity measure to capture the actual access behaviour of the web user is imperative for the significant clustering.Methods: In this paper, the concept of an augmented session is used to derive different augmented session dissimilarity measures. The quantitative performance evaluation of different session dissimilarity measures are performed using a relational fuzzy c-medoid clustering approach. The intra-cluster and inter-cluster distance based cluster quality ratio is used for performance evaluation. Findings: The experimental results demonstrated that augmented web user session dissimilarity in general, and intuitive augmented session dissimilarity, in particular, performed better than the other dissimilarity measures. Improvements: It is argued that augmented session similarity measures are more realistic and represent session similarities based on the web user’s habits, interest, and expectations as compared to simple binary session similarity measures.

Large scale face identification by combined iconic features and 3D joint invariant signatures

In this paper, we present a 2D/3D multimodal face identification system. A set of iconic fiducial points and descriptors is first extracted from the images of the faces and a preliminary correspondence between the points is established on the basis of the descriptor content. Subsequently, the points are mapped on the scans and used to calculate 3D joint differential invariant vectors that define a signature of the face. Since a correspondence between the invariants is inherited from the 2D feature point matching, the signatures of the faces can be efficiently compared by evaluating the distance between corresponding vectors, thus validating the 2D matching hypothesis. This methodology guarantees an effective and fast alignment of the 3D scans, avoids iterative registration procedures and provides a simple similarity measure for face identification. Extensive tests were carried out on the FRGCv2 and on the Bosphorus databases, which both contain 3D and texture information of faces. Results show that the method is robust to expressions provided the images are of good quality, and that it is particularly suited to identification tasks in the cases of medium to large databases with multiple gallery enrolment. Indeed, in these scenarios, the performance was superior or comparable to state of the art methods, with execution times often faster by several orders of magnitude.