33,157 research outputs found
Higher order feature extraction and selection for robust human gesture recognition using CSI of COTS Wi-Fi devices
Device-free human gesture recognition (HGR) using commercial o the shelf (COTS) Wi-Fi
devices has gained attention with recent advances in wireless technology. HGR recognizes the human
activity performed, by capturing the reflections ofWi-Fi signals from moving humans and storing
them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction
and transformation to pre-process the raw CSI traces. However, these methods fail to capture
the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal
representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts
higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the
recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order
cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods
derived from information theory construct a robust and highly informative feature subset, fed as
input to the multilevel support vector machine (SVM) classifier in order to measure the performance.
The proposed methodology is validated using a public database SignFi, consisting of 276 gestures
with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home
environment using a 10 5 cross-validation. HOS-Re achieved an average recognition accuracy of
97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average
recognition accuracy for 150 sign gestures with 7500 instances, collected from five di erent users was
96.23% in the laboratory environment.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio
High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso
The goal of supervised feature selection is to find a subset of input
features that are responsible for predicting output values. The least absolute
shrinkage and selection operator (Lasso) allows computationally efficient
feature selection based on linear dependency between input features and output
values. In this paper, we consider a feature-wise kernelized Lasso for
capturing non-linear input-output dependency. We first show that, with
particular choices of kernel functions, non-redundant features with strong
statistical dependence on output values can be found in terms of kernel-based
independence measures. We then show that the globally optimal solution can be
efficiently computed; this makes the approach scalable to high-dimensional
problems. The effectiveness of the proposed method is demonstrated through
feature selection experiments with thousands of features.Comment: 18 page
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