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With the explosive increase of data volume, the research of data quality and data usability draws extensive attention. In this work, we focus on one aspect of data usability -- incomplete data imputation, and present a novel missing value imputation method using stacked auto-encoder and incremental clustering (SAICI). Specifically, SAICI's functionality rests on four pillars: (i) a distinctive value...
Complex networks refer to large-scale graphs with nontrivial connection patterns. The salient and interesting features that the complex network study offers in comparison to graph theory are the emphasis on the dynamical properties of the networks and the ability of inherently uncovering pattern formation of the vertices. In this paper, we present a hybrid data classification technique combining a...
In this paper, we aim to study the usage of different network formation methods into a graph embedding framework to perform supervised dimensionality reduction. Images are often high-dimensional patterns, and dimensionality reduction can enhance processing and also increase classification accuracy. Specifically, our technique maps images into networks and constructs two network adjacency matrices...
Outlier detection is a classical topic of data mining acting as an essential task for discovering knowledge. Its aim is to detect patterns that deviate from normal behaviour. Numerous outlier detection techniques have been developed but little work has been done in the context of semi-supervised learning. Semi-supervised outlier detection techniques are relatively new and include some labels of normal...
Semi-supervised learning algorithms address the problem of learning from partially labeled data. However, most of the semi-supervised classification methods proposed in the literature considers a stationary distribution of data. Which means that future data patterns tend to conform to the data distribution presented in data set throughout the application lifetime. However, for plenty of new variety...
Traditional data classification considers only physical features (e.g., geometrical or statistical features) of the input data. Here, it is referred to low level classification. In contrast, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only...
How to measure the similarity of data objects is one of the most important problems in the data analysis. This paper proposes a method which uses only information of the distribution of attributes to measure the similarity between nominal data objects. In this algorithm, we made the logarithm form of the conditional probability the main interest, because we think that the distribution information...
This paper takes one kind of 3-TPT Parallel Machine Tool (PMT) as the object to study. It mainly analyzes the influence of the posture error of PMT movable platform on its position error. Firstly, on the basis of introducing the structure of PMT, the kinematics of this machine is analyzed, and the kinematics equation of the parallel mechanism is obtained. Then the influence situation of the posture...
This paper presents a new unified feature parametrization approach for monocular SLAM. The parametrization is based on the parallax angle and can reliably represent both nearby and distant features, as well as features in the direction of camera motion and features observed only once. A new bundle adjustment (BA) algorithm using the proposed parallax angle parametrization is developed and shown to...
Taiwan Futures Exchange (TAIFEX) is the world's 23rd futures exchange to trade SSFs, which is also the 21st financial product offered by the exchange. In this paper, the TAIFEX is predicted based on improving fuzzy time series model. Nature-ratio lengths of intervals technique is employed to partition the universal of discourse of linguistic variable and an improving high-order heuristic function...
This paper proposes a novel distributed training method of Conditional Random Fields (CRFs) by utilizing the clusters built from commodity computers. The method employs Message Passing Interface (MPI) to deal with large-scale data in two steps. Firstly, the entire training data is divided into several small pieces, each of which can be handled by one node. Secondly, instead of adopting a root node...
In this paper, we propose a new method for enrollments prediction, based on fuzzy time series. The new method constructs high-order fuzzy logical relationships with high-order heuristic function based on the historical data and uses nature-ratio techniques to partition the length of each interval in the universe of discourse for enrollments forecasting to increase the prediction accuracy rate. The...
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