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Found 19 papers, 5 papers with code
Clustering Change Sign Detection by Fusing Mixture Complexity
In this paper, we propose MC fusion as an extension of MC to handle situations in which multiple mixture numbers are possible in a finite mixture model.
Balancing Summarization and Change Detection in Graph Streams
The parameter specifying the summary graph is then optimized so that the accuracy of change detection is guaranteed to suppress Type I error probability (probability of raising false alarms) to be less than a given confidence level.
Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds
Machine learning for point clouds has been attracting much attention, with many applications in various fields, such as shape recognition and material science.
Tight and fast generalization error bound of graph embedding in metric space
However, recent theoretical analyses have shown a much higher upper bound on non-Euclidean graph embedding's generalization error than Euclidean one's, where a high generalization error indicates that the incompleteness and noise in the data can significantly damage learning performance.
Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality
Continuous model selection is to determine the real-valued model dimensionality in terms of Ddim from a given data.
Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic
Graph embedding, which represents real-world entities in a mathematical space, has enabled numerous applications such as analyzing natural languages, social networks, biochemical networks, and knowledge bases. It has been experimentally shown that graph embedding in hyperbolic space can represent hierarchical tree-like data more effectively than embedding in linear space, owing to hyperbolic space's exponential growth property.
Generalization Error Bound for Hyperbolic Ordinal Embedding
Hyperbolic ordinal embedding (HOE) represents entities as points in hyperbolic space so that they agree as well as possible with given constraints in the form of entity i is more similar to entity j than to entity k. It has been experimentally shown that HOE can obtain representations of hierarchical data such as a knowledge base and a citation network effectively, owing to hyperbolic space's exponential growth property.
Detecting Hierarchical Changes in Latent Variable Models
This paper addresses the issue of detecting hierarchical changes in latent variable models (HCDL) from data streams.
Word2vec Skip-gram Dimensionality Selection via Sequential Normalized Maximum Likelihood
Therefore, we apply information criteria with the aim of selecting the best dimensionality so that the corresponding model can be as close as possible to the true distribution.
A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification
In this paper, we thus propose a novel global spatial attention mechanism in CNNs mainly for medical image classification.
Online Robust and Adaptive Learning from Data Streams
We address the relation between the two parameters: one is the step size of the stochastic approximation, and the other is the threshold parameter of the norm of the stochastic update.
Mixture Complexity and Its Application to Gradual Clustering Change Detection
Meanwhile, we consider the clustering changes to be gradual in terms of MC; it has the benefits of finding the changes earlier and discerning the significant and insignificant changes.
Descriptive Dimensionality and Its Characterization of MDL-based Learning and Change Detection
The paper also derives error probabilities of the MDL-based test for multiple model change detection.
Riemannian TransE: Multi-relational Graph Embedding in Non-Euclidean Space
Multi-relational graph embedding which aims at achieving effective representations with reduced low-dimensional parameters, has been widely used in knowledge base completion.
Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional $\ell_1$-Balls via Envelope Complexity
The resulting regret bound is so simple that it is completely determined with the smoothness of the loss function and the radius of the balls except with logarithmic factors, and it has a generalized form of existing regret/risk bounds.
Stable Geodesic Update on Hyperbolic Space and its Application to Poincare Embeddings
A hyperbolic space has been shown to be more capable of modeling complex networks than a Euclidean space.
High-dimensional Penalty Selection via Minimum Description Length Principle
In this situation, the luckiness-normalized-maximum-likelihood(LNML)-minimization approach is favorable, because LNML quantifies the goodness of regularized models with any forms of penalty functions in view of the minimum description length principle, and guides us to a good penalty function through the high-dimensional space.
Grafting for Combinatorial Boolean Model using Frequent Itemset Mining
To overcome this computational difficulty, we propose an algorithm GRAB (GRAfting for Boolean datasets), which efficiently learns CBM within the $L_1$-regularized loss minimization framework.
Predicting Glaucoma Visual Field Loss by Hierarchically Aggregating Clustering-based Predictors
Thus, we propose a method for aggregating cluster-based predictors to obtain better prediction accuracy than from a single cluster-based prediction.
