Paper detects changes in graph-based data streams using likelihood-ratios.
problem Detecting changes in synchronized graph-based data streams.
method Kernel-based likelihood-ratio estimation over graph nodes.
result Effective detection and localization of change-points.
A method detects changes in heterogeneous data streams over graph nodes.
problem Detecting changes in data streams from nodes of a graph.
method Online non-parametric method using likelihood-ratio estimation.
result The method accurately identifies change-points in real-world applications.
Graph-based methods for anomaly detection and semi-supervised learning.
problem Detecting unusual clinical actions and anomalies in hospital data.
method Label propagation, harmonic solution, regularization, graph connectivity analysis.
result Effective anomaly detection and semi-supervised learning methods for healthcare data.
Graph-based multimodal federated learning for HAR improves accuracy and privacy.
problem Challenges in HAR due to noisy data, incomplete measurements, and privacy concerns.
method Proposes GraMFedDHAR, a Graph-based Multimodal Federated Learning framework for HAR tasks, using modality-specific graphs, residual GCNs, and attention-based fusion.
result Experimental results show up to 13 percent performance improvement for MultiModalGCN under differential privacy constraints.
Framework for dynamic node embeddings from graph streams.
problem Temporal prediction-based applications using graph stream data.
method ε-graph time-series representation, temporal reachability graphs, weighted temporal summary graphs.
result Dynamic embedding methods achieve better predictive performance.
Following a particular news story online is an important but difficult task, as the relevant information is often scattered across different domains/sources (e.g., news articles, blogs, comments, tweets), presented in various formats and language styles, and may overlap with thousands of other stories. In this work we …
Recommending appropriate items to users is crucial in many e-commerce platforms that contain implicit data as users' browsing, purchasing and streaming history. One common approach consists in selecting the N most relevant items to each user, for a given N, which is called top-N recommendation. To do so, recommender sy…
Bayesian analysis shows unlabeled data improve graph-based semi-supervised learning.
problem Improving semi-supervised learning with limited labeled data.
method Bayesian nonparametric approach using unlabeled data for graph-based learning.
result Posterior contracts optimally around the truth with sufficient unlabeled data.
Data stream classification methods demonstrate promising performance on a single data stream by exploring the cohesion in the data stream. However, multiple data streams that involve several correlated data streams are common in many practical scenarios, which can be viewed as multi-task data streams. Instead of handli…
Data stream clustering tackles real-time data processing challenges.
problem Real-time processing of data streams with less prior information.
method Review of data stream clustering algorithms and their characteristics.
result Comparison and analysis of data stream clustering algorithms.
Bayesian tensor train method recovers streaming data with high accuracy.
problem Recovering high-order, incomplete, and noisy streaming data.
method Bayesian tensor train decomposition using streaming variational Bayes method.
result The proposed SPTT algorithm excels in recovering streaming data compared to state-of-the-art methods.
New algorithm improves graph-based active learning by identifying unexplored regions.
problem Improving graph-based active learning by identifying unexplored regions.
method Poisson Reweighted Laplacian Uncertainty Sampling (PWLL) with a diagonal perturbation.
result PWLL effectively identifies unexplored regions in graph-based data.
Nowadays, every device connected to the Internet generates an ever-growing stream of data (formally, unbounded). Machine Learning on unbounded data streams is a grand challenge due to its resource constraints. In fact, standard machine learning techniques are not able to deal with data whose statistics is subject to gr…
Survey of methods to incorporate external knowledge into stock price prediction.
problem Challenges in predicting stock prices due to market volatility and non-linearity.
method Survey of methods for acquiring and incorporating external knowledge into stock price prediction models.
result Systematic synthesis of previous studies on external knowledge types and their application in stock price prediction.
stream-learn is a Python package compatible with scikit-learn and developed for the drifting and imbalanced data stream analysis. Its main component is a stream generator, which allows to produce a synthetic data stream that may incorporate each of the three main concept drift types (i.e. sudden, gradual and incrementa…
Dynamic Model Tree improves online learning for evolving data streams.
problem Effective and transparent machine learning on data streams is challenging.
method Revisit Model Trees for data stream applications, introducing Dynamic Model Tree.
result Dynamic Model Tree reduces the number of splits and outperforms state-of-the-art models.
The paper extends graph-based semi-supervised learning to infinite-dimensional Wasserstein space.
problem Graph-based semi-supervised learning in high-dimensional data.
method Laplace Learning in the Wasserstein space, proving variational convergence and characterizing the Laplace-Beltrami operator.
result Consistent classification performance in high-dimensional settings.
Proposes CI-GMVC to improve graph-based multi-view clustering performance.
problem Inconsistency in multi-view data affects clustering performance.
method Integrates consistent and inconsistent parts of multiple views using a unified matrix.
result Demonstrates improved clustering performance on real-world datasets.
Adapts DPMM for fast streaming data clustering.
problem Clustering streaming data with time-dependent statistics.
method Adapts DPMM and sampling-based inference for online clustering.
result Obtains state-of-the-art results in speed and accuracy.
CROC identifies the earliest-changing stream as the root cause in multi-stream data.
problem Distribution-free root cause analysis in multi-stream data with unknown distributional changes.
method Conformal p-values and finite-sample valid confidence sets.
result CROC efficiently isolates the root cause under minimal assumptions.
In machine learning, the one-class classification problem occurs when training instances are only available from one class. It has been observed that making use of this class's structure, or its different contexts, may improve one-class classifier performance. Although this observation has been demonstrated for static …
This research generates synthetic data streams for handling concept drifts and novel classes.
problem Handling concept drifts and novel classes in dynamic data streams.
method Synthetic data stream generation for both concept drifts and novel classes.
result Demonstrates the effectiveness of unsupervised drift detectors in open set recognition.
Paper improves Oja's algorithm for Markovian data streams.
problem Estimating the top eigenvector of a covariance matrix from Markovian data.
method Improves Oja's algorithm for streaming PCA with Markovian dependence.
result First sharp rate for Oja's algorithm on entire data stream, removing sample size dependence.
We introduce a new sub-linear space sketch---the Weight-Median Sketch---for learning compressed linear classifiers over data streams while supporting the efficient recovery of large-magnitude weights in the model. This enables memory-limited execution of several statistical analyses over streams, including online featu…
Paper proposes a method to estimate coverage in data streams.
problem Estimating coverage in data streams with limited storage.
method Modified CVM algorithm for estimating coverage in streaming settings.
result The method efficiently estimates coverage in data streams.
Event detection has been one of the most important research topics in social media analysis. Most of the traditional approaches detect events based on fixed temporal and spatial resolutions, while in reality events of different scales usually occur simultaneously, namely, they span different intervals in time and space…
This paper introduces a novel graph signal processing framework for building graph-based models from classes of filtered signals. In our framework, graph-based modeling is formulated as a graph system identification problem, where the goal is to learn a weighted graph (a graph Laplacian matrix) and a graph-based filter…
GraphFL tackles semi-supervised node classification on graphs using federated learning.
problem Real-world graph-based problems often require collecting the entire graph and labeling a reasonable number of labels, which is impractical and costly.
method GraphFL is a federated learning framework that addresses non-IID data, new label domains, and unlabeled data issues in graph-based semi-supervised node classification.
result GraphFL significantly outperforms compared FL baselines and self-training methods.
A new method for estimating SW from streaming data.
problem Estimating Wasserstein distance from sample streams.
method Introducing a streaming estimator of the 1DW and applying it to all projections.
result Stream-SW achieves more accurate approximation of SW than random subsampling.
CURIE uses cellular automata to detect concept drift in data streams.
problem Detecting changes in data distribution (concept drift) in data streams.
method CURIE represents data stream distribution in a cellular automata grid and uses its neighborhood rule to detect changes.
result CURIE, when hybridized with base learners, performs competitively in detection metrics and classification accuracy.
Differentially private ensemble classifiers adapt to data streams while protecting privacy.
problem Adapting to evolving data characteristics while protecting private information.
method Unbounded ensemble updates, model agnostic approach.
result Outperforms competitors on various privacy, drift, and distribution settings.
Visual analytics tool detects and corrects concept drift in data streams.
problem Concept drift causes inaccurate predictions in evolving data.
method DriftVis combines drift detection and visualization.
result Visual analytics supports detection, examination, and correction of concept drift.
Enhash detects concept drift in data streams quickly and efficiently.
problem Detecting abrupt, gradual, virtual, or recurring events in data streams.
method Uses projection hash to insert incoming samples and detects concept drift.
result Enhash has competitive performance and moderate resource requirements compared to existing ensemble learners.
Many modern data analysis problems involve inferences from streaming data. However, streaming data is not easily amenable to the standard probabilistic modeling approaches, which assume that we condition on finite data. We develop population variational Bayes, a new approach for using Bayesian modeling to analyze strea…
Detects synchronized behavior in streaming data.
problem Tracking synchronized behavior in time-stamped tuples.
method AugSplicing algorithm for streaming dense block detection.
result Effective and robust in detecting anomalous behavior.
Paper addresses challenges in benchmarking stream learning algorithms with real-world data.
problem Lack of publicly available non-stationary real-world datasets for evaluating stream algorithms.
method Proposes a new public data repository for benchmarking stream algorithms with real-world data.
result Mitigates problems related to dataset choice in experimental evaluation of stream classifiers and drift detectors.
The performance of classification algorithms with a massive and highly imbalanced data stream depends upon efficient balancing strategy. Some techniques of balancing strategy have been applied in the past with Batch data to resolve the class imbalance problem. This paper proposes a new incremental data balancing framew…
New robustness certificates for streaming models with a sliding window.
problem Applying robustness certificates to streaming data with correlated inputs.
method Deriving robustness certificates for models using a sliding window over a sequence of potentially correlated inputs.
result Guarantees hold for the average model performance across the entire stream, independent of stream size.
This paper reviews methods for distributed training of machine learning models from high-rate streams.
problem Training machine learning models from high-rate distributed streams in a compute- and bandwidth-limited setting.
method Recently developed methods for large-scale distributed stochastic optimization.
result There exist regimes where systems can learn from distributed, streaming data at order-optimal rates.
A new graph-based clustering method for moderate-dimensional data.
problem Performance degradation of existing graph-based clustering methods in high dimensions.
method Introduces UN-CCDs using NND-based MC-SRT for covering radii determination.
result UN-CCDs provide stable and competitive performance in moderate-sized datasets.
A new algorithm for K-means clustering in evolving data streams.
problem Clustering of continuously arriving data in streaming scenarios with concept drift.
method Formal definition of Streaming K-means, surrogate error function, algorithm for minimizing surrogate error. result The surrogate error function effectively approximates the Streaming K-means error. New metrics needed for streaming ML due to delayed labels.
problem Streaming ML evaluation fails to identify unexpected performance.
method Recommend additional metrics for streaming ML performance.
result New metrics are needed for streaming ML due to delayed labels.
We consider a family of problems that are concerned about making predictions for the majority of unlabeled, graph-structured data samples based on a small proportion of labeled samples. Relational information among the data samples, often encoded in the graph/network structure, is shown to be helpful for these semi-sup…
The advance of modern sensor technologies enables collection of multi-stream longitudinal data where multiple signals from different units are collected in real-time. In this article, we present a non-parametric approach to predict the evolution of multi-stream longitudinal data for an in-service unit through borrowing…
Graph-based Bayesian SSL uses graph theory to propagate labels from a few to many unlabeled features.
problem Efficiently propagating labels from a small set of labeled data to a large set of unlabeled data.
method Probabilistic framework using graph theory and Bayesian statistics.
result Mathematical foundations for improving the accuracy and efficiency of label propagation.
New algorithm samples matrix rows proportional to their ℓ_p norm in a turnstile data stream.
problem Sampling rows of a dynamic matrix efficiently in a turnstile data stream.
method Develops a novel algorithm for sampling rows proportional to their ℓ_p norm in a turnstile data stream, returning sampled row indexes and approximated sampling probabilities.
result Achieves (1+ε) approximation for logistic regression in a turnstile data stream with polynomial sketch size. METEOR learns efficient representations from multi-modal data streams.
problem Efficiently interpreting multi-modal information in complex environments.
method METEOR learns compact representations by sharing parameters within semantically meaningful groups and preserving domain-agnostic semantics.
result METEOR reduces memory usage by around 80% compared to conventional methods.
Federated learning for data streams tackles real-time learning from IoT devices.
problem Efficiently learn from data streams generated by IoT devices and smartphones.
method Proposes a general federated learning algorithm for data streams using weighted empirical risk minimization.
result Demonstrates improved performance on various machine learning tasks compared to static dataset approaches.