Hybrid clustering combines partitional and hierarchical clustering for computational effectiveness and versatility in cluster shape. In such clustering, a dissimilarity measure plays a crucial role in the hierarchical merging. The dissimilarity measure has great impact on the final clustering, and data-independent prop…
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48 results for “dissimilarity measures”
DID measures similarity invariant to diffeomorphisms.
problem Measuring similarity invariance to diffeomorphisms.
method DID measures similarity as the solution to an optimization problem in a Reproducing Kernel Hilbert Space.
result DID is invariant to diffeomorphisms and can be efficiently approximated.
FPI methods compute barycenters of Gaussian sets for various dissimilarity measures.
problem Efficiently compute barycenters of Gaussian sets for multiple dissimilarity measures.
method Fixed-Point Iterations (FPI) for several dissimilarity measures.
result FPI provides a useful toolbox for fusion/reduction of Gaussian sets.
Random Forest proximity measures for multi-view classification.
problem Combining multiple heterogeneous data views for classification.
method Building dissimilarity representations for each view, fusing them dynamically.
result Dynamic View Selection improves multi-view classification performance.
New RDPC dissimilarity measure improves time series clustering.
problem Improving time series clustering methods for diverse data.
method Combining weighted Pearson correlation with largest element-wise differences.
result RDPC outperforms existing methods in complex datasets.
New RF dissimilarity measures improve multi-view learning accuracy.
problem Improving multi-view learning accuracy in HDLSS problems.
method Modified Random Forest proximity measures for HDLSS multi-view classification.
result Second method significantly more accurate than other state-of-the-art methods.
Paper proposes a new efficient transport-based dissimilarity measure for time series classification.
problem Classifying time series with warping distortions.
method Defining a problem statement, proposing an Optimal Transport-based dissimilarity measure.
result The proposed method can solve the time series classification problem with reduced computational cost.
We introduce a new unsupervised learning problem: clustering wide-sense stationary ergodic stochastic processes. A covariance-based dissimilarity measure together with asymptotically consistent algorithms is designed for clustering offline and online datasets, respectively. We also suggest a formal criterion on the eff…
We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. Similarity-based anomaly detection algorithms detect abnormally large amounts of similarity or dissimilarity, e.g.~as measured by nearest neighbor Euclidean distances between a test sam…
Improves Gower's similarity for mixed-type variables with automatic weighting.
problem Handling missing values and unbalanced variable contributions in Gower's similarity for mixed-type data.
method Automatic weighting scheme minimizing differences in correlation between contributing dissimilarities and weighted Gower's dissimilarity.
result Improved performance in classification and imputation of missing values.
We propose an iterative scheme for feature-based positioning using a new weighted dissimilarity measure with the goal of reducing the impact of large errors among the measured or modeled features. The weights are computed from the location-dependent standard deviations of the features and stored as part of the referenc…
We propose a novel method to determine the dissimilarity between subjects for functional data clustering. Spline smoothing or interpolation is common to deal with data of such type. Instead of estimating the best-representing curve for each subject as fixed during clustering, we measure the dissimilarity between subjec…
We consider general non-Euclidean distance measures between real world objects that need to be classified. It is assumed that objects are represented by distances to other objects only. Conditions for zero-error dissimilarity based classifiers are derived. Additional conditions are given under which the zero-error deci…
The one-class classification problem is a well-known research endeavor in pattern recognition. The problem is also known under different names, such as outlier and novelty/anomaly detection. The core of the problem consists in modeling and recognizing patterns belonging only to a so-called target class. All other patte…
Paper introduces k-DTW for robust curve comparison.
problem Robust dissimilarity measure for polygonal curves.
method Introduces k-Dynamic Time Warping (k-DTW) as a novel dissimilarity measure.
result k-DTW is more robust to outliers and has stronger metric properties than DTW.
Unified model for interactive estimation with improved learnability measure.
problem Improving learnability in interactive estimation models.
method Introducing a combinatorial measure (dissimilarity dimension) and a general algorithm with polynomial bounds.
result Unified model subsumes statistical-query learning and structured bandits.
This paper presents a new methodology for clustering multivariate time series leveraging optimal transport between copulas. Copulas are used to encode both (i) intra-dependence of a multivariate time series, and (ii) inter-dependence between two time series. Then, optimal copula transport allows us to define two distan…
Simplified proof for dimension reduction of polygonal curves.
problem Preserving the continuous Fréchet distance of polygonal curves.
method Sparse oblivious subspace embeddings for generalized dissimilarity measures.
result Generalized dimension reduction technique works for various distance measures.
Quantitatively assessing relationships between latent variables and observed variables is important for understanding and developing generative models and representation learning. In this paper, we propose latent-observed dissimilarity (LOD) to evaluate the dissimilarity between the probabilistic characteristics of lat…
Locality sensitive hashing (LSH) is a powerful tool for sublinear-time approximate nearest neighbor search, and a variety of hashing schemes have been proposed for different dissimilarity measures. However, hash codes significantly depend on the dissimilarity, which prohibits users from adjusting the dissimilarity at q…
In numerous applicative contexts, data are too rich and too complex to be represented by numerical vectors. A general approach to extend machine learning and data mining techniques to such data is to really on a dissimilarity or on a kernel that measures how different or similar two objects are. This approach has been …
A non-vector-based dissimilarity measure is proposed by combining vector-based distance metrics and set operations. This proposed compound dissimilarity measure (CDM) is applicable to quantify similarity of collections of attribute/feature pairs where not all attributes are present in all collections. This is a typical…
BERT improved for propaganda detection with imbalanced, dissimilar data.
problem BERT struggles with dissimilar imbalanced datasets in propaganda detection.
method Cost-sensitive BERT with dissimilarity measure for imbalanced, dissimilar datasets.
result Achieved second-highest score on sentence-level propaganda classification.
RFSVM uses learned RF similarity for HDLSS classification.
problem High dimension, low sample size classification problems.
method Transposes RFD approach to HDLSS classification using RF similarity as SVM kernel.
result RFSVM outperforms existing methods for HDLSS problems.
Method reveals dissimilarity in alloys' Curie temperatures.
problem Tackles the dissimilarity between rare-earth transition metal binary alloys.
method Ensemble learning with Kernel ridge regression.
result Reveals meaningful relations between alloys' structure and Curie temperature.
Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality Reductionstat.ML
SQFA learns features maximizing Fisher-Rao distance for better classification.
problem Improving classification accuracy through feature learning.
method SQFA learns linear features maximizing Fisher-Rao distance between class-conditional distributions.
result SQFA-H features achieve the best classification accuracy.
This paper presents a distance-based discriminative framework for learning with probability distributions. Instead of using kernel mean embeddings or generalized radial basis kernels, we introduce embeddings based on dissimilarity of distributions to some reference distributions denoted as templates. Our framework exte…
Improved binning technique boosts nUV measure performance.
problem Improving the performance of the nUV measure in real applications.
method Introduced the nUV measure, provided theoretical optimal binning techniques, and proposed algorithms for approximate solutions.
result Approximate binning techniques show 4-13% increase in AUC scores with statistical significance.
Unified framework for robust clustering under various dissimilarity measures.
problem Improving center-based clustering methods to handle outliers and non-Euclidean data.
method Median-of-Means (MoM) estimation framework with uniform concentration bounds.
result Strong consistency and error rate of under mild conditions.
Affinity propagation is one of the most effective unsupervised pattern recognition algorithms for data clustering in high-dimensional feature space. However, the numerous attempts to test its performance for community detection in complex networks have been attaining results very far from the state of the art methods s…
Deep neural networks perform well on real world data but are prone to adversarial perturbations: small changes in the input easily lead to misclassification. In this work, we propose an attack methodology not only for cases where the perturbations are measured by norms, but in fact any adversarial dissimilarit…
New framework to test neural network representation similarity measures.
problem Disagreements among dissimilarity measures in neural networks.
method Statistical testing framework to evaluate measures based on functional behavior.
result Current metrics have different weaknesses; a classical baseline performs surprisingly well.
Three bootstrap tests compare categorical time series generating processes.
problem Testing equality of generating processes in categorical time series.
method Proposes three tests using dissimilarity measures and bootstrap methods.
result Advantages and disadvantages of each bootstrap method discussed.
Topolow embeds dissimilarity data into Euclidean space robustly against non-metricity and sparsity.
problem Embedding dissimilarity data into Euclidean space when dissimilarities are non-metric or sparse.
method Topolow uses a physics-inspired, gradient-free optimization framework to maximize likelihood under a Laplace error model.
result Topolow outperforms standard MDS methods in reconstructing sparse and non-Euclidean data.
The development of autonomous robotic systems that can learn from human demonstrations to imitate a desired behavior - rather than being manually programmed - has huge technological potential. One major challenge in imitation learning is the correspondence problem: how to establish corresponding states and actions betw…
This paper considers the problem of low-dimensional visualisation of very high dimensional information sources for the purpose of situation awareness in the maritime environment. In response to the requirement for human decision support aids to reduce information overload (and specifically, data amenable to inter-point…
Partitioning Relational Matrices of Similarities or Dissimilarities using the Value of Informationcs.AI
In this paper, we provide an approach to clustering relational matrices whose entries correspond to either similarities or dissimilarities between objects. Our approach is based on the value of information, a parameterized, information-theoretic criterion that measures the change in costs associated with changes in inf…
Geometric structures and deviations on James' symmetric positive-definite matrix bicone domainstat.ML
New geometric structures defined on SPD matrices for better understanding.
problem Understanding SPD matrices and their geometric properties.
method Introducing Finslerian and dual information-geometric structures on James' bicone domain.
result Geodesics correspond to straight lines in coordinate systems, and new dissimilarities generalize existing ones.
DMAE uses neural networks to cluster data with flexible dissimilarity functions.
problem Clustering data with complex dissimilarity functions.
method Integrates a dissimilarity mixture model into deep learning architectures.
result DMAE achieves competitive clustering accuracy compared to other methods.
Genie clusters faster and resists outliers.
problem Hierarchical clustering's sensitivity to outliers and slow computation.
method Genie uses an economic inequity measure to link clusters, balancing speed and quality.
result Genie outperforms other linkage methods in clustering quality and speed.
Feature Learning for Nonlinear Dimensionality Reduction toward Maximal Extraction of Hidden Patternscs.LG
FEALM learns features for better nonlinear DR of hidden patterns.
problem DR misses important patterns on distorted manifolds.
method FEALM generates optimized projections using an optimization algorithm and neighbor-shape dissimilarity.
result FEALM captures important patterns on hidden manifolds.
A new model encodes distances and topology in latent variables.
problem Modeling dissimilarity data with latent variables and invariances.
method Isometric Gaussian Process Latent Variable Model using Riemannian geometry and variational inference.
result The model can encode invariances in learned manifolds.
E2Tree explains random forest models in regression tasks.
problem Lack of transparency in random forest models.
method E2Tree extends random forest to regression by explaining model predictions through graphical representation and dissimilarity measures.
result E2Tree provides a transparent explanation of random forest models in regression tasks.
We review some developments on clustering stochastic processes and come with the conclusion that asymptotically consistent clustering algorithms can be obtained when the processes are ergodic and the dissimilarity measure satisfies the triangle inequality. Examples are provided when the processes are distribution ergod…
This work examines consistency issues in Gaussian Mixture Model reduction algorithms.
problem Consistency issues in Gaussian Mixture Model reduction algorithms.
method Discussion of the importance of dissimilarity measure choice and consistency of GMR algorithms.
result Most existing GMR algorithms are not consistent with a unique measure, leading to suboptimal reduced GMs.
A new method for automatically aligning and clustering time series data.
problem Challenges in aligning and clustering time series data, especially without a template signal.
method TROUT (Temporal Registration using Optimal Unitary Transformations) method based on a novel dissimilarity measure.
result TROUT outperforms competitors in clustering time series data.
We propose some axioms for hierarchical clustering of probability measures and investigate their ramifications. The basic idea is to let the user stipulate the clusters for some elementary measures. This is done without the need of any notion of metric, similarity or dissimilarity. Our main results then show that for e…
This paper reviews the economic and theoretical foundations of insolvency risk measurement and capital adequacy rules. The proposed new measure of insolvency risk is constructed by disentangling assets, debt and equity at the micro-prudential firm level. This new risk index is the Firm Insolvency Risk Index (FIRI) whic…