New features capture the order of data streams.
problem Handling ordered moments in massive data streams.
method Introducing features for ordered moments.
result Theoretical guarantees for learning algorithms.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,181 papers · 148 categories
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48 results for “stream features”
Sketches linear classifiers using Weight-Median Sketch for efficient data stream analysis.
problem Efficiently learning and analyzing data streams with limited memory.
method Introduces Weight-Median Sketch for compressed linear classifier learning over data streams.
result Memory-limited execution of various analyses over streams, including feature selection and mutual information estimation.
New algorithm reduces kernel PCA to features for streaming data.
problem Efficiently performing kernel PCA on large datasets.
method Random Fourier features and Oja's algorithm for streaming.
result Achieves sample complexity with features.
As an emerging research direction, online streaming feature selection deals with sequentially added dimensions in a feature space while the number of data instances is fixed. Online streaming feature selection provides a new, complementary algorithmic methodology to enrich online feature selection, especially targets t…
New algorithm improves feature selection for streaming data.
problem Traditional OSFS methods assume all data available at runtime, but features and samples stream concurrently.
method Introduces Geometric Online Adaption (GOA) for concurrent streaming of features and samples.
result GOA outperforms SAOLA on various datasets and in the OSFS-SS setting.
New approach for feature evolution in streaming data with limited storage.
problem Rarely-provided labels in feature evolving streams.
method Incorporates manifold regularization and a buffer to adapt to different storage budgets.
result Preserves the performance of feature evolving learning across different storage budgets.
Learning with streaming data has attracted much attention during the past few years. Though most studies consider data stream with fixed features, in real practice the features may be evolvable. For example, features of data gathered by limited-lifespan sensors will change when these sensors are substituted by new ones…
AdapVAE learns streaming data clustering and feature learning adaptively.
problem Adaptive clustering and feature learning for streaming data.
method Bayesian Nonparametric (BNP) modeling with Deep Neural Networks (DNNs) for feature learning, online variational inference algorithm.
result AdapVAE can adaptively detect novel clusters in emerging data without catastrophic forgetting.
A new method detects anomalies in multivariate streams without unit dependence.
problem Detect anomalies in multivariate streams without unit dependence.
method Proposes SigMahaKNN combining variance norm and path signature.
result SigMahaKNN detects anomalies better than existing methods.
Extracts behavioral features from smartphone and wearable data.
problem Processing raw data streams from smartphones and wearables for human behavior analysis.
method Generic framework for processing raw data streams and extracting behavioral features.
result Extracts useful features related to non-verbal human behavior from raw data streams.
New online feature selection method handles streaming data with concept drift.
problem Handling streaming data with concept drift and sparsity.
method Online feature screening method with model adaptation.
result Online screening methods with model adaptation outperform without model adaptation on data streams with concept drift.
DPASF stream preprocesses Big Data streams efficiently.
problem Efficient preprocessing of streaming Big Data.
method Implemented six preprocessing algorithms in Apache Flink.
result Preprocessing improves data accuracy in streaming Big Data.
Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streamscs.LG
DEVDAN adapts to evolving data streams by automatically adding or removing features.
problem Adapting Denoising Autoencoder to rapidly changing data streams.
method DEVDAN features an open structure with an NS method for automatic feature addition or removal.
result DEVDAN improves classification accuracy on non-stationary data streams.
A novel approach for cost-sensitive feature acquisition in data streams.
problem Learning scenarios with feature acquisition at a cost.
method Incremental feature acquisition using a context-aware feature-value function in a reinforcement learning paradigm.
result The proposed method efficiently acquires features and makes accurate predictions.
Paper classifies multiple video sources in encrypted tunnels using NLP-inspired features.
problem Traffic classification in encrypted video streams.
method Deep learning with a novel NLP-inspired feature for multi-label classification.
result The method achieves high performance on binary and multilabel classification tasks.
Online learning algorithm identifies contextual changes for streaming data.
problem Challenges in identifying and utilizing contextual cues for streaming data.
method Neural network-based autoencoder for identifying contextual changes; comparison to a knowledge base of learned contexts.
result Classifier training benefits from automatically discovered contexts, demonstrating quicker learning convergence during contextual changes.
We present a scheme for online, unsupervised state discovery and detection from streaming, multi-featured, asynchronous data in high-frequency financial markets. Online feature correlations are computed using an unbiased, lossless Fourier estimator. A high-speed maximum likelihood clustering algorithm is then used to f…
Paper tackles class-incremental time series classification with dual-stream feature extraction.
problem Class-incremental continual learning for multivariate time series data.
method Dual-stream feature extraction pipeline combining deep temporal embedding features and statistical features.
result Competitive average accuracy across multiple datasets with low forgetting rates.
Paper tackles online budgeted learning for feature acquisition in machine learning.
problem Finding optimal feature values to acquire from each instance in a data stream under budget constraints.
method Introduces a general framework for online budgeted learning, proposing two feature value acquisition policies: random and adaptive.
result Adaptive policies outperform random policies for most budget limitations and datasets, achieving near-optimal results in some cases.
A new framework for efficient streaming network embedding.
problem Efficiently embedding new vertices in growing networks.
method Adapting existing network embedding methods to streaming environments.
result Provable feasible and close to optimal latent features for new vertices.
CDLEEDS detects local changes in evolving data streams for accurate feature attributions.
problem Local feature attributions become obsolete in evolving data streams.
method CDLEEDS, a flexible framework for detecting local change and concept drift.
result CDLEEDS reliably detects both local and global concept drift.
New algorithm learns reliable regression coefficients from streaming data with partial features and adversarial corruption.
problem Learning reliable regression coefficients from streaming data with partial features and adversarial corruption.
method RoOFS algorithm that iteratively updates regression coefficients and uncorrupted feature set via robust online feature substitution.
result RoOFS algorithm has a restricted error bound compared to the optimal solution and outperforms existing methods in feature selection and regression coefficient recovery.
Learning from the past, predicting the statistics for the future, learning an evolving systemq-fin.ST
We bring the theory of rough paths to the study of non-parametric statistics on streamed data. We discuss the problem of regression where the input variable is a stream of information, and the dependent response is also (potentially) a stream. A certain graded feature set of a stream, known in the rough path literature…
Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of feature stream. For instance, in image analysis, features are generated in groups which represent color, texture…
The Acoustic Camouflage Phenomenon: Re-evaluating Speech Features for Financial Risk Predictioncs.SD
Study shows integrating acoustic features in financial forecasting models can degrade performance.
problem Predicting stock market volatility from corporate earnings calls using speech features.
method Empirical investigation of acoustic feature extraction in teleconference environments using a two-stream late-fusion architecture.
result Integrating acoustic features via late fusion significantly degraded performance, reducing recall to 47.08%.
Recognising human activities from streaming videos poses unique challenges to learning algorithms: predictive models need to be scalable, incrementally trainable, and must remain bounded in size even when the data stream is arbitrarily long. Furthermore, as parameter tuning is problematic in a streaming setting, suitab…
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.
Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streamscs.LG
DSSCN improves lifelong learning of non-stationary data streams through adaptive network construction.
problem Lifelong learning of non-stationary data streams with efficient and adaptive models.
method Deep stacked stochastic configuration network (DSSCN) with self-constructing deep stacked network structure and adaptive hidden unit parameters.
result DSSCN outperforms existing data stream algorithms in continual learning of non-stationary data streams.
Data Smashingcs.LG
Investigation of the underlying physics or biology from empirical data requires a quantifiable notion of similarity - when do two observed data sets indicate nearly identical generating processes, and when they do not. The discriminating characteristics to look for in data is often determined by heuristics designed by …
Kernel principal component analysis (KPCA) provides a concise set of basis vectors which capture non-linear structures within large data sets, and is a central tool in data analysis and learning. To allow for non-linear relations, typically a full kernel matrix is constructed over data points, but this…
OMF algorithm adapts to Markovian data streams for efficient feature extraction.
problem Convergence of OMF algorithms for dependent data streams.
method Non-convex generalization of OMF for Markovian data, convergence to critical points.
result OMF converges to critical points of expected loss function for Markovian data.
Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams.
problem Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams.
method Multi-Stream Fraud Transformer (MSFT) architecture that encodes each event stream with independent Transformer encoders and fuses their representations through configurable mechanisms.
result Sequence models significantly outperform gradient-boosted trees operating on aggregated features.
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.
Deterministic algorithm estimates ridge regression with minimal space.
problem Estimating ridge regression solutions efficiently.
method Deterministic space-efficient algorithm using Frequent Directions.
result First space deterministic streaming algorithm with guaranteed error.
A new data-level recombination strategy improves RGB-D salient object detection.
problem RGB-D salient object detection struggles with depth information.
method Proposes a novel data-level recombination strategy to fuse RGB and depth data before feature extraction.
result Achieves a new state-of-the-art performance in RGB-D salient object detection.
A novel online feature selection method using DPP for diversity.
problem Online feature selection for diverse feature sets.
method DPP-based framework with three stages: sampling, local criteria, and global criteria.
result Demonstrated better compactness and comparable/outsuperior performance.
Improved BIRCH clustering method to avoid numeric issues.
problem Numeric instability in BIRCH clustering.
method Introduced a new cluster feature to replace the sum of squares, avoiding catastrophic cancellation.
result The new method is more numerically stable and efficient.
A new method for real-time CCA on streaming data.
problem Finding correlated features in online data streams.
method Sliding Window Informative Canonical Correlation Analysis (SWICCA) using streaming PCA.
result SWICCA provides real-time CCA components in high dimensions with theoretical guarantees.
Paper combines RNN and signatures for learning functions on streamed multimodal data.
problem Learning functions on streamed multimodal data.
method Hybrid Logsig-RNN algorithm combining signatures and RNN.
result Hybrid algorithm achieves outstanding accuracy with superior efficiency and robustness.
InQMAD detects anomalies in streaming data using quantum measurements and density matrices.
problem Detecting anomalies in streaming data with challenges like conceptual drift and continuous learning.
method Incremental anomaly detection based on random Fourier features and quantum measurements.
result InQMAD outperforms 12 state-of-the-art methods in a systematic evaluation.
Approach to detect and adapt to concept drift in unlabeled streaming data.
problem Detect and adapt to concept drift in high-dimensional, noisy, low-context data.
method Density-based clustering for virtual drift and weak supervision for real drift.
result 90% precision in detecting and adapting to concept drift for 4 years after initial deployment.
Probabilistic hash embeddings improve online learning of categorical features.
problem Online learning of categorical features with changing vocabulary.
method Probabilistic hash embedding (PHE) with Bayesian online learning.
result PHE mitigates forgetting and maintains high performance in online settings.
A Federated Data Fusion-Based Prognostic Model for Applications with Multi-Stream Incomplete Signalsstat.ML
A federated model predicts failures using multi-stream incomplete data.
problem Insufficient data for reliable prognostic models in multi-stream applications.
method Federated data fusion, multivariate functional principal component analysis, (log)-location-scale regression model, federated algorithm.
result Performance is as good as classic non-federated models and better than individual models.
Model improves information transfer from visual streams.
problem Challenges in unsupervised learning from continuous visual data.
method Inspired by physics, maximizes mutual information through temporal process.
result Focus of attention enhances information transfer from input stream.
Paper introduces a streaming compression method for monitoring pedestrian events on footbridges.
problem Storage and analysis of high-rate sensor data from instrumented infrastructure is computationally challenging.
method Develops a streaming feature-based compression method to preserve key patterns and features of pedestrian events.
result Demonstrates the trade-off between compression and accuracy during and between pedestrian events.
We present a sparse analogue to stochastic gradient descent that is guaranteed to perform well under similar conditions to the lasso. In the linear regression setup with irrepresentable noise features, our algorithm recovers the support set of the optimal parameter vector with high probability, and achieves a statistic…
Adapting deep learning for object detection to detect mixed image tampering.
problem Detecting mixed image tampering without prior knowledge of the method.
method Adapting deep learning for object detection to learn from a large database of various image types, using a Multi-stream Faster RCNN network with fused features from ELA and BAG error maps.
result Improved accuracy in detecting mixed image tampering.
Two-stream model recognizes affect from audio and video.
problem Human affect recognition in real-world settings.
method Two-stream aural-visual analysis model with separate audio and visual processing.
result Model achieves promising results on Aff-Wild2 database.