26,096 research outputs found
Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition
This paper presents a novel quadratic projection based feature extraction
framework, where a set of quadratic matrices is learned to distinguish each
class from all other classes. We formulate quadratic matrix learning (QML) as a
standard semidefinite programming (SDP) problem. However, the con- ventional
interior-point SDP solvers do not scale well to the problem of QML for
high-dimensional data. To solve the scalability of QML, we develop an efficient
algorithm, termed DualQML, based on the Lagrange duality theory, to extract
nonlinear features. To evaluate the feasibility and effectiveness of the
proposed framework, we conduct extensive experiments on biometric recognition.
Experimental results on three representative biometric recogni- tion tasks,
including face, palmprint, and ear recognition, demonstrate the superiority of
the DualQML-based feature extraction algorithm compared to the current
state-of-the-art algorithm
A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables
It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications
Bi-Objective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models
Nonnegative matrix factorization (NMF) is a powerful class of feature
extraction techniques that has been successfully applied in many fields, namely
in signal and image processing. Current NMF techniques have been limited to a
single-objective problem in either its linear or nonlinear kernel-based
formulation. In this paper, we propose to revisit the NMF as a multi-objective
problem, in particular a bi-objective one, where the objective functions
defined in both input and feature spaces are taken into account. By taking the
advantage of the sum-weighted method from the literature of multi-objective
optimization, the proposed bi-objective NMF determines a set of nondominated,
Pareto optimal, solutions instead of a single optimal decomposition. Moreover,
the corresponding Pareto front is studied and approximated. Experimental
results on unmixing real hyperspectral images confirm the efficiency of the
proposed bi-objective NMF compared with the state-of-the-art methods
Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization
Nonnegative Matrix Factorization (NMF) has been continuously evolving in
several areas like pattern recognition and information retrieval methods. It
factorizes a matrix into a product of 2 low-rank non-negative matrices that
will define parts-based, and linear representation of nonnegative data.
Recently, Graph regularized NMF (GrNMF) is proposed to find a compact
representation,which uncovers the hidden semantics and simultaneously respects
the intrinsic geometric structure. In GNMF, an affinity graph is constructed
from the original data space to encode the geometrical information. In this
paper, we propose a novel idea which engages a Multiple Kernel Learning
approach into refining the graph structure that reflects the factorization of
the matrix and the new data space. The GrNMF is improved by utilizing the graph
refined by the kernel learning, and then a novel kernel learning method is
introduced under the GrNMF framework. Our approach shows encouraging results of
the proposed algorithm in comparison to the state-of-the-art clustering
algorithms like NMF, GrNMF, SVD etc.Comment: This paper has been withdrawn by the author due to the terrible
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Can smartwatches replace smartphones for posture tracking?
This paper introduces a human posture tracking platform to identify the human postures of sitting, standing or lying down, based on a smartwatch. This work develops such a system as a proof-of-concept study to investigate a smartwatch's ability to be used in future remote health monitoring systems and applications. This work validates the smartwatches' ability to track the posture of users accurately in a laboratory setting while reducing the sampling rate to potentially improve battery life, the first steps in verifying that such a system would work in future clinical settings. The algorithm developed classifies the transitions between three posture states of sitting, standing and lying down, by identifying these transition movements, as well as other movements that might be mistaken for these transitions. The system is trained and developed on a Samsung Galaxy Gear smartwatch, and the algorithm was validated through a leave-one-subject-out cross-validation of 20 subjects. The system can identify the appropriate transitions at only 10 Hz with an F-score of 0.930, indicating its ability to effectively replace smart phones, if needed
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