This paper presents a new insight into improving the performance of Stochastic Neighbour Embedding (t-SNE) by using Isolation kernel instead of Gaussian kernel. Isolation kernel outperforms Gaussian kernel in two aspects. First, the use of Isolation kernel in t-SNE overcomes the drawback of misrepresenting some structu…
iMondrian forest combines isolation forest and Mondrian forest for better anomaly detection.
problem Anomaly detection in batch and online settings.
method Hybrid of isolation forest and Mondrian forest, using depth in Mondrian forest structure.
result iMondrian forest outperforms existing methods in batch and online settings.
A new framework detects anomalies in structured data.
problem Detecting anomalies in samples not conforming to low-dimensional manifolds.
method Preference Isolation Forest (PIF) framework combining adaptive isolation methods and preference embedding.
result Anomalies identified as isolated points in a high-dimensional preference space.
Two new scoring methods improve anomaly detection in Isolation Forest.
problem Anomaly and outlier detection in data.
method Generalized score function and volume-based scoring for individual trees.
result Significant improvement in anomaly detection on 34 datasets.
The paper identifies all link projections with isolate-region number one.
problem Determining link projections with a specific isolate-region number.
method Analyzing link projections to find isolated regions and their cardinality.
result All link projections with isolate-region number one are identified.
This work briefly explores the possibility of approximating spatial distance (alternatively, similarity) between data points using the Isolation Forest method envisioned for outlier detection. The logic is similar to that of isolation: the more similar or closer two points are, the more random splits it will take to se…
Localization of unknown faults in industrial systems is a difficult task for data-driven diagnosis methods. The classification performance of many machine learning methods relies on the quality of training data. Unknown faults, for example faults not represented in training data, can be detected using, for example, ano…
A recent proposal of data dependent similarity called Isolation Kernel/Similarity has enabled SVM to produce better classification accuracy. We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead. We formally prove the characteristic of Isolatio…
Wavelet analysis reveals limitations in detecting multifractality in signals with isolated singularities.
problem Detecting multifractality in signals with isolated singularities using detrended fluctuation analysis and wavelet leaders.
method Comparison of detrended fluctuation analysis and wavelet leaders on signals with isolated singularities.
result Signals with isolated singularities can artefactually give rise to broad multifractal spectra, leading to incorrect inference of multifractality.
Proposes methods to improve interpretability of Isolation Forest for anomaly detection.
problem Lack of interpretability in Isolation Forest.
method Defines feature importance scores and unsupervised feature selection methods.
result Improves interpretability of Isolation Forest for anomaly detection.
Improved isolation forest for better outlier detection.
problem Outlier detection in multivariate data.
method Random cuts across feature space, size of feature space, and point assignment information.
result Improved results in many situations without modifying tree structure.
We present an extension to the model-free anomaly detection algorithm, Isolation Forest. This extension, named Extended Isolation Forest (EIF), resolves issues with assignment of anomaly score to given data points. We motivate the problem using heat maps for anomaly scores. These maps suffer from artifacts generated by…
Signature Isolation Forest removes constraints from FIF by using rough path theory's signature transform.
problem Challenges in FIF's linear inner product and dictionary choices leading to unreliable results.
method Introduces Signature Isolation Forest using rough path theory's signature transform to remove linearity constraints.
result Demonstrates relevance of methods through numerical experiments and real-world applications.
Paper improves anomaly detection by using non-uniform random choices in isolation forests.
problem Detecting clustered diverse outliers more effectively.
method Comparing different split guiding criteria in isolation forests.
result Non-uniform random choices improve outlier discrimination for certain outlier classes.
Develops a machine learning method for parameter estimation in branching processes models.
problem Parameter evaluation for unevenly distributed sparse and dense regions in stochastic datasets.
method Approximate Bayesian computation based on Isolation Kernel mapping and maxima weighted kernel.
result Effective parameter estimation for cancer cell evolution models using personal data.
The paper classifies circle actions on 6D manifolds with isolated fixed points.
problem Classifying circle actions on 6D manifolds with isolated fixed points.
method Performing equivariant connected sums at fixed points with specific manifolds.
result A sequence of operations can reduce the fixed point data to the empty collection.
NestedVAE isolates common factors from paired images without additional supervision.
problem Reduction of data-driven biases in machine learning models.
method Combines deep latent variable models with information bottleneck theory.
result NestedVAE significantly outperforms alternative methods in various tasks.
Study of G2-structures with isolated singularities and bounded torsion.
problem Understanding G2-structures with special torsion and isolated singularities. method Revisiting known examples, describing symmetries, and analyzing collapsing of circle fibres.
result Collapsing circle fibres at isolated points cannot produce G2-structures with bounded torsion. Paper describes anomaly detection and explainability for multivariate functional data.
problem Anomaly detection and explainability in multivariate functional data.
method Transform series into features, use Isolation Forest, compute SHAP coefficients, and use supervised decision tree.
result Method performs well on simulated and real industry data.
New stability and isolation results for Einstein manifolds.
problem Stability and isolation of Einstein manifolds.
method Conditions on Weyl tensor for AH and ALE manifolds, Bochner tensor for Kähler and Sasaki manifolds.
result Established new stability criteria and isolation results for various types of Einstein manifolds.
Paper introduces AIF for anomaly detection with variable feature sensitivity.
problem Lack of variable sensitivity in anomaly detection methods.
method Extended Isolation Forest with feature sensitivities (Anisotropic Isolation Forest).
result AIF enables anomaly detection with controllable sensitivity to different features.
Map quandle orders to actions, characterize isolated orders, and prove no isolated right orders.
problem Characterizing isolated orders on quandles and proving the absence of certain orders.
method Construct a continuous map from orders to actions, use strong rigidity, and analyze specific cases.
result No isolated right orders on free quandles, except for specific cases.
FLBench automates federated learning benchmarking.
problem Manual dataset partitioning fails to simulate real-world isolated data islands.
method Develops a federated learning benchmark suite with three domains.
result Automates evaluation of federated learning systems and algorithms.
VFGNN tackles privacy-preserving node classification with federated GNN.
problem Data isolation problem in graph data.
method Vertically partitioned federated GNN, differential privacy.
result Demonstrates effectiveness of VFGNN on three benchmarks.
Defines Perelman's functionals on manifolds with non-isolated conical singularities.
problem Defining functionals on manifolds with non-isolated conical singularities.
method Starting from a spectral point of view for the Perelman's λ-functional, defining the spectrum of Schrödinger operator and proving the existence of discrete eigenvalues.
result Proves the existence of the infimum of W-functional and obtains asymptotic behavior of eigenfunctions.
Develops a formalism for studying general horizons and derives a near-horizon equation.
problem Analyzes the geometry of general horizons in spacetime.
method Introduces a formalism based on encoding the zeroth and first transverse derivatives of the deformation tensor on null hypersurfaces.
result Derives a generalized near-horizon equation that holds on any horizon.
MIK improves t-SNE's local structure preservation in biological sequence data.
problem Efficiently preserving local structure in high-dimensional biological sequence data.
method Modified Isolation Kernel (MIK) using adaptive density estimation.
result MIK preserves local and global structure better than Gaussian and isolation kernels.
We investigate the face numbers of simplicial complexes with Buchsbaum vertex links, especially pseudomanifolds with isolated singularities. This includes deriving Dehn-Sommerville relations for pseudomanifolds with isolated singularities and establishing lower bound theorems when the singularities are also homological…
We explore the geometry of nonpositively curved spaces with isolated flats, and its consequences for groups that act properly discontinuously, cocompactly, and isometrically on such spaces. We prove that the geometric boundary of the space is an invariant of the group up to equivariant homeomorphism. We also prove that…
The Poincaré-Hopf theorem is extended to projective varieties with isolated singularities.
problem Extending the Poincaré-Hopf theorem to varieties with isolated singularities.
method Using generalizations of the Poincaré-Hopf index.
result A Poincaré-Hopf type theorem for projective varieties with isolated singularities.
Quantifies how geodesic planes isolate in hyperbolic 3-manifolds.
problem Understanding isolation properties of geodesic planes in hyperbolic 3-manifolds.
method Quantitative estimates of geodesic planes in frame bundles, using tight areas and densities.
result Polynomial estimates of isolation properties with degree given by modified critical exponents.
RAID algorithm detects anomalies in real-time IoT systems.
problem Anomaly detection limitations in multivariate dynamic processes.
method Adapts to non-stationary effects and handles data drift.
result Improved detection accuracy and root cause isolation.
We define two types of local indices of a vector field at an isolated zero on the boundary, and prove Poincare-Hopf-type index theorems for certain vector fields on a compact smooth manifold which have only isolated zeros.
Study reveals GAGA phenomenon in Poisson cohomology for plane structures with isolated singularities.
problem Understanding Poisson cohomology for plane structures with isolated singularities.
method Determined Gerstenhaber algebra structure over Poisson cohomology groups.
result GAGA type phenomenon observed in Poisson cohomology.
Proves conditions for positive scalar curvature on certain manifolds with conical singularities.
problem Conditions for positive scalar curvature on manifolds with isolated conical singularities.
method Analyzes isolated conical singularities and uses Geroch type results.
result No metric with positive scalar curvature on X#Tn with isolated conical singularity. We study the Euler obstruction of essentially isolated determinantal singularities (EIDS). The EIDS were defined by W. Ebeling and S. Gusein-Zade, as a generalization of isolated singularity. We obtain some formulas to calculate the Euler obstruction for the determinantal varieties with singular set an ICIS.
The paper builds complex hyperbolic 2-manifolds with isolated singularities.
problem Finding compact complex hyperbolic 2-manifolds with non-free actions and isolated fixed points.
method Constructs specific examples for each prime p with Z/pZ action. result General examples for p=2 related to complex hyperbolic lattices conjugacy separability. The Poincaré-Hopf theorem is extended to projective varieties with isolated singularities.
problem Extending the Poincaré-Hopf theorem to projective varieties with isolated singularities.
method Using generalized Poincaré-Hopf indices for a projective variety with isolated determinantal singularities.
result A Poincaré-Hopf type theorem is proven for projective varieties with isolated singularities.
Robert Bryant (Theorie des varietes minimales et applications, 1988, 154: 321-347) proved that an isolated singularity of a conformal metric of positive constant curvature on a Riemann surface is a conical one. Using Complex Analysis, we find all of the local models for an isolated singularity of a flat metric whose ar…
Large scale online kernel learning aims to build an efficient and scalable kernel-based predictive model incrementally from a sequence of potentially infinite data points. A current key approach focuses on ways to produce an approximate finite-dimensional feature map, assuming that the kernel used has a feature map wit…
Study on solutions near isolated singularities in 6D Yamabe equation.
problem Behavior of solutions near isolated singularities in 6D Yamabe equation.
method Analyze asymptotic behavior of local solutions in non-conformally flat metrics.
result Solutions are asymptotically close to Fowler solutions in 6D.
We study solutions to conformally invariant equations with isolated singularties.
The purpose of this erratum is to correct the proof of Theorem A.0.1 in the appendix to our article ``Hadamard spaces with isolated flats'' math.GR/0411232, which was jointly authored by Mohamad Hindawi, Hruska and Kleiner. In that appendix, many of the results of math.GR/0411232 about CAT(0) spaces with isolated flats…
Enhanced Extended Isolation Forest (EIF+) improves anomaly detection and provides interpretable explanations.
problem Detecting anomalies in complex datasets and explaining model predictions.
method Extended Isolation Forest (EIF) and Extended Isolation Forest Feature Importance (ExIFFI) methods.
result EIF+ outperforms EIF in detecting unseen anomalies and provides better generalization.
New isolated geometric triangulations found in once-punctured torus bundles.
problem Identifying isolated geometric triangulations in 3-manifolds.
method Examining ideal triangulations and their moves to find isolated geometric ones.
result Infinite family of once-punctured torus bundles with isolated geometric triangulations.
Score-based methods fail with isolated components and incorrect mixing proportions.
problem Score-based methods struggle with distributions having isolated components and incorrect mixing proportions.
method Score-based methods, including score matching, are used but fail in the presence of isolated components and incorrect mixing proportions.
result Score-based methods cannot discover isolated components or identify correct mixing proportions.
ABIForest improves anomaly detection using attention weights.
problem Anomaly detection in datasets.
method Attention mechanism integrated into Isolation Forest.
result ABIForest outperforms standard Isolation Forest on synthetic and real datasets.
Paper develops a new local convexity condition for non-isolated minima in non-convex optimization.
problem Lack of theory for non-isolated minima in non-convex optimization.
method Formulates a new local convexity condition and studies SGD convergence under this condition.
result Shows SGD can converge locally under the new condition.