Nguyen Lu Dang Khoa

Large Scale Spectral Clustering Using Resistance Distance and Spielman-Teng Solvers

The promise of spectral clustering is that it can help detect complex shapes and intrinsic manifold structure in large and high dimensional spaces. The price for this promise is the computational cost O(n 3) for computing the eigen-decomposition of the graph Laplacian matrix - so far a necessary subroutine for spectral clustering. In this paper we bypass the eigen-decomposition of the original Laplacian matrix by leveraging the recently introduced Spielman and Teng near-linear time solver for systems of linear equations and random projection. Experiments on several synthetic and real datasets show that the proposed approach has better clustering quality and is faster than the state-of-the-art approximate spectral clustering methods.

On Damage Identification in Civil Structures Using Tensor Analysis

Structural health monitoring is a condition-based technology to monitor infrastructure using sensing systems. In structural health monitoring, the data are usually highly redundant and correlated. The measured variables are not only correlated with each other at a certain time but also are autocorrelated themselves over time. Matrix-based two-way analysis, which is usually used in structural health monitoring, can not capture all these relationships and correlations together. Tensor analysis allows us to analyse the vibration data in temporal, spatial and feature modes at the same time. In our approach, we use tensor analysis and one-class support vector machine for damage detection, localization and estimation in an unsupervised manner. The method shows promising results using data from lab-based structures and also data collected from the Sydney Harbour Bridge, one of iconic structures in Australia. We can obtain a damage detection accuracy of 0.98 and higher for all the data. Locations of damage were captured correctly and different levels of damage severity were well estimated.

A scalable approach to spectral clustering with SDD solvers

The promise of spectral clustering is that it can help detect complex shapes and intrinsic manifold structure in large and high dimensional spaces. The price for this promise is the expensive computational cost for computing the eigen-decomposition of the graph Laplacian matrix—so far a necessary subroutine for spectral clustering. In this paper we bypass the eigen-decomposition of the original Laplacian matrix by leveraging the recently introduced near-linear time solver for symmetric diagonally dominant (SDD) linear systems and random projection. Experiments on several synthetic and real datasets show that the proposed approach has better clustering quality and is faster than the state-of-the-art approximate spectral clustering methods.

Network Anomaly Detection Using a Commute Distance Based Approach

We propose the use of commute distance, a random walk metric, to discover anomalies in network traffic data. The commute distance based anomaly detection approach has several advantages over Principal Component Analysis (PCA), which is the method of choice for this task: (i) It generalizes both distance and density based anomaly detection techniques while PCA is primarily distance-based (ii) It is agnostic about the underlying data distribution, while PCA is based on the assumption that data follows a Gaussian distribution and (iii) It is more robust compared to PCA, i.e., a perturbation of the underlying data or changes in parameters used will have a less significant effect on the output of it than PCA. Experiments and analysis on simulated and real datasets are used to validate our claims.

A Tensor-Based Structural Damage Identification and Severity Assessment

Early damage detection is critical for a large set of global ageing infrastructure. Structural Health Monitoring systems provide a sensor-based quantitative and objective approach to continuously monitor these structures, as opposed to traditional engineering visual inspection. Analysing these sensed data is one of the major Structural Health Monitoring (SHM) challenges. This paper presents a novel algorithm to detect and assess damage in structures such as bridges. This method applies tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies, i.e., structural damage. To evaluate this approach, we collected acceleration data from a sensor-based SHM system, which we deployed on a real bridge and on a laboratory specimen. The results show that our tensor method outperforms a state-of-the-art approach using the wavelet energy spectrum of the measured data. In the specimen case, our approach succeeded in detecting 92.5% of induced damage cases, as opposed to 61.1% for the wavelet-based approach. While our method was applied to bridges, its algorithm and computation can be used on other structures or sensor-data analysis problems, which involve large series of correlated data from multiple sensors.

Adaptive One-Class Support Vector Machine for Damage Detection in Structural Health Monitoring

Machine learning algorithms have been employed extensively in the area of structural health monitoring to compare new measurements with baselines to detect any structural change. One-class support vector machine (OCSVM) with Gaussian kernel function is a promising machine learning method which can learn only from one class data and then classify any new query samples. However, generalization performance of OCSVM is profoundly influenced by its Gaussian model parameter \(\sigma \). This paper proposes a new algorithm named Appropriate Distance to the Enclosing Surface (ADES) for tuning the Gaussian model parameter. The semantic idea of this algorithm is based on inspecting the spatial locations of the edge and interior samples, and their distances to the enclosing surface of OCSVM. The algorithm selects the optimal value of \(\sigma \) which generates a hyperplane that is maximally distant from the interior samples but close to the edge samples. The sets of interior and edge samples are identified using a hard margin linear support vector machine. The algorithm was successfully validated using sensing data collected from the Sydney Harbour Bridge, in addition to five public datasets. The designed ADES algorithm is an appropriate choice to identify the optimal value of \(\sigma \) for OCSVM especially in high dimensional datasets.

Smart Infrastructure Maintenance Using Incremental Tensor Analysis: Extended Abstract

Civil infrastructures are key to the flow of people and goods in urban environments. Structural Health Monitoring (SHM) is a condition-based maintenance technology, which provides and predicts actionable information on the current and future states of infrastructures. SHM data are usually multi-way data which are produced by multiple highly correlated sensors. Tensor decomposition allows the learning from such data in temporal, spatial and feature modes at the same time. However, to facilitate a real time response for online learning, incremental tensor update need to be used when new data come in, rather than doing the decomposition in a batch manner. This work proposed a method called onlineCP-ALS to incrementally update tensor component matrices, followed by a self-tuning one-class support vector machine for online damage identification. Moreover, a robust clustering technique was applied on the tensor space for online substructure grouping and anomaly detection. These methods were applied to data from lab-based structures and also data collected from the Sydney Harbour Bridge in Australia. We obtained accurate damage detection accuracies for all these datasets. Damage locations were also captured correctly, and different levels of damage severity were well estimated. Furthermore, the clustering technique was able to detect spatial anomalies, which were associated with sensor and instrumentation issues. Our proposed method was efficient and much faster than the batch approach.

Self-advised Incremental One-Class Support Vector Machines: An Application in Structural Health Monitoring

Incremental One-Class Support Vector Machine (OCSVM) methods provide critical advantages in practical applications, as they are able to capture variations of the positive samples over time. This paper proposes a novel self-advised incremental OCSVM algorithm, which decides whether an incremental step is required to update its model or not. As opposed to existing method, this novel online algorithm does not rely on any fixed threshold, but it uses the slack variables in the OCSVM as proxies for data in order to determine which new data points should be included in the training set and trigger an update of the model’s coefficients. This new online OCSVM algorithm was extensively evaluated using real data from Structural Health Monitoring (SHM) case studies. These results showed that this new online method provided significant improvements in classification error rates, was able to assimilate the changes in the positive data distribution over the time, and maintained a high damage detection accuracy in these SHM cases.

Incremental Commute Time Using Random Walks and Online Anomaly Detection

Commute time is a random walk based metric on graphs and has found widespread successful applications in many application domains. However, the computation of the commute time is expensive, involving the eigen decomposition of the graph Laplacian matrix. There has been effort to approximate the commute time in offline mode. Our interest is inspired by the use of commute time in online mode. We propose an accurate and efficient approximation for computing the commute time in an incremental fashion in order to facilitate real-time applications. An online anomaly detection technique is designed where the commute time of each new arriving data point to any data point in the current graph can be estimated in constant time ensuring a real-time response. The proposed approach shows its high accuracy and efficiency in many synthetic and real datasets and takes only 8 milliseconds on average to detect anomalies online on the DBLP graph which has more than 600,000 nodes and 2 millions edges.

On Structural Health Monitoring Using Tensor Analysis and Support Vector Machine with Artificial Negative Data

Structural health monitoring is a condition-based technology to monitor infrastructure using sensing systems. Since we usually only have data associated with the healthy state of a structure, one-class approaches are more practical. However, tuning the parameters for one-class techniques (like one-class Support Vector Machines) still remains a relatively open and difficult problem. Moreover, in structural health monitoring, data are usually multi-way, highly redundant and correlated, which a matrix-based two-way approach cannot capture all these relationships and correlations together. Tensor analysis allows us to analyse the multi-way vibration data at the same time. In our approach, we propose the use of tensor learning and support vector machines with artificial negative data generated by density estimation techniques for damage detection, localization and estimation in a one-class manner. The artificial negative data can help tuning SVM parameters and calibrating probabilistic outputs, which is not possible to do with one-class SVM. The proposed method shows promising results using data from laboratory-based structures and also with data collected from the Sydney Harbour Bridge, one of the most iconic structures in Australia. The method works better than the one-class approach and the approach without using tensor analysis.