9 research outputs found
Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data
This paper addresses the learning task of estimating driver drowsiness from
the signals of car acceleration sensors. Since even drivers themselves cannot
perceive their own drowsiness in a timely manner unless they use burdensome
invasive sensors, obtaining labeled training data for each timestamp is not a
realistic goal. To deal with this difficulty, we formulate the task as a weakly
supervised learning. We only need to add labels for each complete trip, not for
every timestamp independently. By assuming that some aspects of driver
drowsiness increase over time due to tiredness, we formulate an algorithm that
can learn from such weakly labeled data. We derive a scalable stochastic
optimization method as a way of implementing the algorithm. Numerical
experiments on real driving datasets demonstrate the advantages of our
algorithm against baseline methods.Comment: Accepted by ICASSP202
The Mahalanobis-Taguchi system based on statistical modeling
早大学位記番号:新7809早稲田大
Proximity-Based Anomaly Detection using Sparse Structure Learning
We consider the task of performing anomaly detection in highly noisy multivariate data. In many applications involving real-valued time-series data, such as physical sensor data and economic metrics, discovering changes and anomalies in the way variables depend on one another is of particular importance. Our goal is to robustly compute the “correlation anomaly ” score of each variable by comparing the test data with reference data, even when some of the variables are highly correlated (and thus collinearity exists). To remove seeming dependencies introduced by noise, we focus on the most significant dependencies for each variable. We perform this “neighborhood selection ” in an adaptive manner by fitting a sparse graphical Gaussian model. Instead of traditional covariance selection procedures, we solve this problem as maximum likelihood estimation of the precision matrix (inverse covariance matrix) under the L1 penalty. Then the anomaly score for each variable is computed by evaluating the distances between the fitted conditional distributions within the Markov blanket for that variable, for the (two) data sets to be compared. Using real-world data, we demonstrate that our matrix-based sparse structure learning approach successfully detects correlation anomalies under collinearities and heavy noise.