Other affiliations: University of Southern Queensland, University of New England (United States), University of New England (Australia) ...read more
Bio: Xiaodi Huang is an academic researcher from Charles Sturt University. The author has contributed to research in topics: Graph drawing & Cluster analysis. The author has an hindex of 22, co-authored 121 publications receiving 1777 citations. Previous affiliations of Xiaodi Huang include University of Southern Queensland & University of New England (United States).
Papers published on a yearly basis
TL;DR: This paper study subspace clustering for multi-view data while keeping individual views well encapsulated, and presents a novel objective function coupled with an angular based regularizer that refines the angular-based data correlation.
Abstract: More often than not, a multimedia data described by multiple features, such as color and shape features, can be naturally decomposed of multi-views. Since multi-views provide complementary information to each other, great endeavors have been dedicated by leveraging multiple views instead of a single view to achieve the better clustering performance. To effectively exploit data correlation consensus among multi-views, in this paper, we study subspace clustering for multi-view data while keeping individual views well encapsulated. For characterizing data correlations, we generate a similarity matrix in a way that high affinity values are assigned to data objects within the same subspace across views, while the correlations among data objects from distinct subspaces are minimized. Before generating this matrix, however, we should consider that multi-view data in practice might be corrupted by noise. The corrupted data will significantly downgrade clustering results. We first present a novel objective function coupled with an angular based regularizer. By minimizing this function, multiple sparse vectors are obtained for each data object as its multiple representations. In fact, these sparse vectors result from reaching data correlation consensus on all views. For tackling noise corruption, we present a sparsity-based approach that refines the angular-based data correlation. Using this approach, a more ideal data similarity matrix is generated for multi-view data. Spectral clustering is then applied to the similarity matrix to obtain the final subspace clustering. Extensive experiments have been conducted to validate the effectiveness of our proposed approach.
TL;DR: An overview of the wide varieties of techniques based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black- box’ will give a detailed understanding about seizure detection and classification, and research directions in the future.
Abstract: Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain signals produced by brain neurons. Neurons are connected to each other in a complex way to communicate with human organs and generate signals. The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media. These signals are complex, noisy, non-linear, non-stationary and produce a high volume of data. Hence, the detection of seizures and discovery of the brain-related knowledge is a challenging task. Machine learning classifiers are able to classify EEG data and detect seizures along with revealing relevant sensible patterns without compromising performance. As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features. The main challenges are selecting appropriate classifiers and features. The aim of this paper is to present an overview of the wide varieties of these techniques over the last few years based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black-box’. The presented state-of-the-art methods and ideas will give a detailed understanding about seizure detection and classification, and research directions in the future.
TL;DR: In this article, a simplified equation for determining the transverse electromagnetic mode (TEM) power penetration depth of microwaves in materials having both magnetic and dielectric response was derived.
Abstract: A simplified equation for determining the transverse electromagnetic mode (TEM) power penetration depth of microwaves in materials having both magnetic and dielectric response was derived. The penetration depths for a magnetite concentrate were calculated using this “full-response” equation, and a significant difference is shown compared with the penetration depth obtained using only the dielectric response (i.e., assuming no imaginary part of complex relative permeability). The temperature dependence of the power penetration depth, up to 1000°C, was determined using measured values of the real and imaginary parts of complex relative permittivity, er′, er″ and permeability, μr′, μr″. The accurate determination of penetration depths can help in optimizing the dimensions of a load in a microwave furnace, producing more uniform heating under microwave irradiation and avoiding thermal runaway.
••01 Dec 2012
TL;DR: A real-time method for wandering detection based on individuals' GPS traces that is able to detect loop-like traces on the fly and is effective and efficient in detecting wandering behaviors.
Abstract: Wandering is among the most frequent, problematic, and dangerous behaviors for elders with dementia. Frequent wanderers likely suffer falls and fractures, which affect the safety and quality of their lives. In order to monitor outdoor wandering of elderly people with dementia, this paper proposes a real-time method for wandering detection based on individuals' GPS traces. By representing wandering traces as loops, the problem of wandering detection is transformed into detecting loops in elders' mobility trajectories. Specifically, the raw GPS data is first preprocessed to remove noisy and crowded points by performing an online mean shift clustering. A novel method called θ_WD is then presented that is able to detect loop-like traces on the fly. The experimental results on the GPS datasets of several elders have show that the θ_WD method is effective and efficient in detecting wandering behaviors, in terms of detection performance (AUC > 0.99, and 90% detection rate with less than 5 % of the false alarm rate), as well as time complexity.
01 Jan 2002
01 Jan 2006
01 Jan 2014
TL;DR: This survey tries to clarify the different problem definitions related to subspace clustering in general; the specific difficulties encountered in this field of research; the varying assumptions, heuristics, and intuitions forming the basis of different approaches; and how several prominent solutions tackle different problems.
Abstract: As a prolific research area in data mining, subspace clustering and related problems induced a vast quantity of proposed solutions. However, many publications compare a new proposition—if at all—with one or two competitors, or even with a so-called “naive” ad hoc solution, but fail to clarify the exact problem definition. As a consequence, even if two solutions are thoroughly compared experimentally, it will often remain unclear whether both solutions tackle the same problem or, if they do, whether they agree in certain tacit assumptions and how such assumptions may influence the outcome of an algorithm. In this survey, we try to clarify: (i) the different problem definitions related to subspace clustering in general; (ii) the specific difficulties encountered in this field of research; (iii) the varying assumptions, heuristics, and intuitions forming the basis of different approaches; and (iv) how several prominent solutions tackle different problems.
01 Jul 2012
TL;DR: The problems motivating subspace clustering are sketched, different definitions and usages of subspaces for clusteringare described, and exemplary algorithmic solutions are discussed.
Abstract: Subspace clustering refers to the task of identifying clusters of similar objects or data records (vectors) where the similarity is defined with respect to a subset of the attributes (i.e., a subspace of the data space). The subspace is not necessarily (and actually is usually not) the same for different clusters within one clustering solution. In this article, the problems motivating subspace clustering are sketched, different definitions and usages of subspaces for clustering are described, and exemplary algorithmic solutions are discussed. Finally, we sketch current research directions. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.