scholarly journals A Harmonic Linear Dynamical System for Prominent ECG Feature Extraction

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ngoc Anh Nguyen Thi ◽  
Hyung-Jeong Yang ◽  
SunHee Kim ◽  
Luu Ngoc Do
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
Feature Extraction ◽  
Biomedical Applications ◽  
Empirical Evaluation ◽  
Computation Time ◽  
Main Stream ◽  
Linear Dynamical System ◽  
Improved Performance ◽  
Real World Datasets

Unsupervised mining of electrocardiography (ECG) time series is a crucial task in biomedical applications. To have efficiency of the clustering results, the prominent features extracted from preprocessing analysis on multiple ECG time series need to be investigated. In this paper, a Harmonic Linear Dynamical System is applied to discover vital prominent features via mining the evolving hidden dynamics and correlations in ECG time series. The discovery of the comprehensible and interpretable features of the proposed feature extraction methodology effectively represents the accuracy and the reliability of clustering results. Particularly, the empirical evaluation results of the proposed method demonstrate the improved performance of clustering compared to the previous main stream feature extraction approaches for ECG time series clustering tasks. Furthermore, the experimental results on real-world datasets show scalability with linear computation time to the duration of the time series.

scholarly journals Improved Linear Dynamical System for Unsupervised Time Series Recognition

2014 ◽  
Vol 10 (1) ◽  
pp. 47-53
Author(s):  
Ngoc Anh Nguyen Thi ◽  
Hyung-Jeong Yang ◽  
Soo-Hyung Kim ◽  
Guee-Sang Lee ◽  
Sun-Hee Kim
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
Linear Dynamical System

scholarly journals Competitive Caching with Machine Learned Advice

2021 ◽  
Vol 68 (4) ◽  
pp. 1-25
Author(s):  
Thodoris Lykouris ◽  
Sergei Vassilvitskii
Keyword(s):  
Online Algorithms ◽  
Empirical Evaluation ◽  
Optimal Solution ◽  
Poor Performance ◽  
Machine Learning Algorithms ◽  
Average Error ◽  
Generalization Error ◽  
Worst Case ◽  
Future Events ◽  
Real World Datasets

Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution, as compared to an offline optimum. On the other hand, machine learning algorithms are in the business of extrapolating patterns found in the data to predict the future, and usually come with strong guarantees on the expected generalization error. In this work, we develop a framework for augmenting online algorithms with a machine learned predictor to achieve competitive ratios that provably improve upon unconditional worst-case lower bounds when the predictor has low error. Our approach treats the predictor as a complete black box and is not dependent on its inner workings or the exact distribution of its errors. We apply this framework to the traditional caching problem—creating an eviction strategy for a cache of size k . We demonstrate that naively following the oracle’s recommendations may lead to very poor performance, even when the average error is quite low. Instead, we show how to modify the Marker algorithm to take into account the predictions and prove that this combined approach achieves a competitive ratio that both (i) decreases as the predictor’s error decreases and (ii) is always capped by O (log k ), which can be achieved without any assistance from the predictor. We complement our results with an empirical evaluation of our algorithm on real-world datasets and show that it performs well empirically even when using simple off-the-shelf predictions.

scholarly journals Adversarial Unsupervised Representation Learning for Activity Time-Series

2019 ◽  
Vol 33 ◽  
pp. 834-841 ◽  
Author(s):  
Karan Aggarwal ◽  
Shafiq Joty ◽  
Luis Fernandez-Luque ◽  
Jaideep Srivastava
Keyword(s):  
Time Series ◽  
Chronic Conditions ◽  
Empirical Evaluation ◽  
Representation Learning ◽  
Time Segment ◽  
Linear Classifiers ◽  
Daily Routines ◽  
Learning Technique ◽  
Prevention And Cure ◽  
Better Than

Sufficient physical activity and restful sleep play a major role in the prevention and cure of many chronic conditions. Being able to proactively screen and monitor such chronic conditions would be a big step forward for overall health. The rapid increase in the popularity of wearable devices pro-vides a significant new source, making it possible to track the user’s lifestyle real-time. In this paper, we propose a novel unsupervised representation learning technique called activ-ity2vecthat learns and “summarizes” the discrete-valued ac-tivity time-series. It learns the representations with three com-ponents: (i) the co-occurrence and magnitude of the activ-ity levels in a time-segment, (ii) neighboring context of the time-segment, and (iii) promoting subject-invariance with ad-versarial training. We evaluate our method on four disorder prediction tasks using linear classifiers. Empirical evaluation demonstrates that our proposed method scales and performs better than many strong baselines. The adversarial regime helps improve the generalizability of our representations by promoting subject invariant features. We also show that using the representations at the level of a day works the best since human activity is structured in terms of daily routines.

Time series forecasting based on wavelet decomposition and feature extraction

2016 ◽  
Vol 28 (S1) ◽  
pp. 183-195 ◽  
Author(s):  
Tianhong Liu ◽  
Haikun Wei ◽  
Chi Zhang ◽  
Kanjian Zhang
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Wavelet Decomposition ◽  
Time Series Forecasting

A Frequency-Domain Filtering Technique for Triple Decomposition of Unsteady Turbulent Flow

1992 ◽  
Vol 114 (1) ◽  
pp. 45-51 ◽  
Author(s):  
G. J. Brereton ◽  
A. Kodal
Keyword(s):  
Time Series ◽  
Turbulent Flow ◽  
Frequency Domain ◽  
Measurement Data ◽  
Averaging Technique ◽  
Phase Averaging ◽  
Spectral Estimates ◽  
Improved Performance ◽  
A New Technique ◽  
Flow Variables

A new technique is presented for decomposing unsteady turbulent flow variables into their organized unsteady and turbulent components, which appears to offer some significant advantages over existing ones. The technique uses power-spectral estimates of data to deduce the optimal frequency-domain filter for determining the organized and turbulent components of a time series of data. When contrasted with the phase-averaging technique, this method can be thought of as replacing the assumption that the organized motion is identically reproduced in successive cycles of known periodicity by a more general condition: the cross-correlation of the organized and turbulent components is minimized for a time series of measurement data, given the expected shape of the turbulence power spectrum. The method is significantly more general than the phase average in its applicability and makes more efficient use of available data. Performance evaluations for time series of unsteady turbulent velocity measurements attest to the accuracy of the technique and illustrate the improved performance of this method over the phase-averaging technique when cycle-to-cycle variations in organized motion are present.

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