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We address two important issues in causal discovery from nonstationary or heterogeneous data, where parameters associated with a causal structure may change over time or across data sets. First, we investigate how to efficiently estimate the "driving force" of the nonstationarity of a causal mechanism. That is, given a causal mechanism that varies over time or across data sets and whose...
In this paper, we address the lack of interpretability of Support Vector Machine (SVM) via rules based on support vectors. The lack of intuitive explanation of the rules in the domain of medical whole slide image classification by determining a reduced subset of Scale-Invariant Feature Transform (SIFT) features and linear dimensionality reduction. This reduced subset of SIFT features that participate...
NB-UVB Phototherapy is one of the most common treatments administrated by dermatologists for psoriasis patients. Although in general, the treatment results in improving the condition, it also can worsen it. If a model can predict the treatment response before hand, the dermatologists can adjust the treatment accordingly. In this paper, we use data mining techniques and conduct four experiments. The...
Algorithms that map graphs into feature vectors encoding the presence/absence of specific subgraphs, have shown excellent performance in various data mining tasks. Discriminative subgraphs have been successfully utilized as features for graphs classification. Most of the existing algorithms mine for discriminative subgraphs that completely appear frequently in graphs belonging to one class label and...
Driven by the dramatic growth of data both in terms of the size and sources, learning from heterogeneous data is emerging as an important research direction for many real applications. One of the biggest challenges of this type of problem is how to meaningfully integrate heterogeneous data to considerably improve the generality and quality of the learning model. In this paper, we first present a unified...
With the rapid advances in IoT technologies, the role of IoT gateways becomes even more important. Therefore, improving the reliability, availability and serviceability (RAS) of IoT gateways is crucial. Nowadays, Linux is widely adopted for core enterprise systems not only because it is a free operating system but also because it offers advantages in regards to operational stability. With many Linux...
Density peak (DP) based clustering algorithm is a recently proposed clustering approach and has been shown to be with great potential. This algorithm is based on the simple assumption that cluster centers have high local density and they are relatively far from each other. This observation is used to isolate cluster centers from other data. By making use of the density relationship among neighboring...
The random Fourier Features method has been found very effective in approximating the kernel functions. Our former studies show that through a mixing mechanism of the feature space formed by random Fourier features and certain linear algorithms, the fuzzy clustering results in the approximated feature space are comparable to or even exceed the classical kernel-based algorithms. To increase the robustness...
Aiming at the problem of mine fault prediction, a fault prediction model based on KPCA and Pearson correlation coefficient is proposed. The model obtains the abnormal sampling data by the kernel principal component method, extracts the abnormal sampling data and draws the contribution plots, then the Pearson correlation coefficient is compared with the existing fault contribution plots. Finally, according...
This paper presents a winning solution to the AAIA'17 Data Mining Challenge. The challenge focused on creating an efficient prediction model for digital card game Hearthstone. Our final solution is an ensemble of various neural network models, including convolutional neural networks.
A kernel or mini-app is a self-contained small application that retains certain characteristics of the original application [7]. Working on a kernel or mini-app in the place of the original application can dramatically reduce the resources and effort required for performing software tasks such as performance optimization and porting to new platforms. However, using kernel as a proxy is based on the...
Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of the input video signals. This integrated solution defines an image descriptor that reflects the global motion information over...
Volterra series have been popular in non-linear system analysis and modeling for a long time. Volterra representation adapts the convolution concept from linear time-invariant (LTI) systems, generalizes it to “super convolutions” and, hence, are able to characterize the dynamical behavior of non-linear circuits using familiar LTI techniques. It is extensively used in white-box verification problems...
High-order Drug-Drug Interactions (DDI) are common particularly for elderly people. It is highly non-trivial to detect such interactions via in vivo/in vitro experiments. In this paper, we present SVM-based classification methods to predict whether a high-order directional drug-drug interaction (HoDDDI) instance is associated with adverse drug reactions (ADRs) and induced side effects. Specifically,...
Neuroscience researchers have a keen interest in finding the connection between various brain regions of an organism. Researchers all across the globe are finding new connections everyday and it is very difficult to keep track of all those, so it is important to create a centralized system which is able to give the relation between brain entities. Databases like PubMed contains abstracts and references...
We proposed a novel method of feature extraction for multi-modal images called modality-convolution. It extracts both the intra- and inter-modality information. Whats more, it completes the data fusion at pixel-level so that the complementarity of information contained in multi-modal data is fully utilized. Based on the modality-convolution, we describe a modality-CNN for multi-modal gesture recognition...
In this paper, we propose a cost function that corresponds to the mean square errors between estimated values and true values of conditional probability in a discrete distribution. We then obtain the values that minimize the cost function. This minimization approach can be regarded as the direct estimation of likelihood ratios because the estimation of conditional probability can be regarded as the...
Owing to prominence as a diagnostic tool for probing the neural correlates of cognition, neuroimaging tensor data has been the focus of intense investigation. Although many supervised tensor learning approaches have been proposed, they either cannot capture the nonlinear relationships of tensor data or cannot preserve the complex multi-way structural information. In this paper, we propose a Multi-way...
In this paper, we propose a no-reference video quality assessment (VQA) method based on Convolutional Neural Network (CNN) and Multi-Regression (CNN-MR). It is universal for non-specific types of distortion. First, we innovatively introduce the 2D convolutional neural network into VQA model to learn the spatial quality features at frame level. Second, the motion information is extracted as temporal...
Extinction profile (EP) is an effective feature extraction method which can well preserve the geometrical characteristics of a hyperspectral image (HSI) and by extracting the EP from first three independent components (ICs) of an HSI, three correlated and complementary groups of EP features can be constructed. In this paper, an EPs fusion (EPs-F) strategy is proposed for HSI classification by exploring...
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