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Personality research on social media is a hot topic recently due to the rapid development of social media as well as the central importance of personality study in psychology, but most studies are conducted on inadequate label samples. Our research aims to explore the usage of unlabeled samples to improve the prediction accuracy. By conducting n user study with 1792 users, we adopt local linear semi-supervised...
Given multiple classifiers, one prevalent approach in classifier ensemble is to diversely combine classifier components (diversity-based ensemble), and a lot of previous works show that this approach can improve accuracy in classification. However, how to measure diversity and perform diversity-based learning are still challenges in the literature. Moreover, the learning procedure highly depends upon...
This paper presents a multi-joint lower limbs rehabilitation robot with three degrees of freedom. The robot includes seat, left mechanical leg, right mechanical leg and electric control box, and each mechanical leg includes the hip joint, knee joint and ankle joint which correspond to the hip joint, knee joint and ankle joint of human. The mechanical structure of the rehabilitation robot is described...
Text mining is a discovery of interesting knowledge in text documents. Exact and accurate knowledge in the text documents needed for the user to find what they require. Many data mining methods are used to mine useful patterns from text documents. However, using and updating these discovered patterns is still an open research issue. Many term based methods are suggested, but a disadvantage with these...
In the Prognostics and Health Management domain, estimating the remaining useful life (RUL) of critical machinery is a challenging task. Various research topics as data acquisition and processing, fusion, diagnostics, prognostivs and decision are involved in this domain. This paper presents an approach for estimating the Remaining Useful Life (RUL) of equipments based on shapelet extraction and characterization...
The timely detection of abnormal energy usage is one of the major ad-hoc techniques to optimize energy efficiency. Typically an alarm is triggered either by a significant drift from the baseline consumption level or by a period of large variations. In this paper we propose a statistical predictive method for detecting anomalies both in mean and in variation. The criterion behind is based on the prediction...
Finding out an effective way to score Chinese written essays automatically remains challenging for researchers. Several methods have been proposed and developed but limited in the character and word usage levels. As one of the scoring standards, however, content or topic perspective is also an important and necessary indicator to assess an essay. Therefore, in this paper, we propose a novel perspective...
In this paper, we show how to improve the Radial Basis Function Neural Networks effectiveness by using the Optimum-Path Forest clustering algorithm, since it computes the number of clusters on-the-fly, which can be very interesting for finding the Gaussians that cover the feature space. Some commonly used approaches for this task, such as the well-known fc-means, require the number of classes/clusters...
In this paper, we propose a Multilayer Markovian model for change detection in registered aerial image pairs with large time differences. A Three Layer Markov Random Field takes into account information from two different sets of features namely the Modified HOG (Histogram of Oriented Gradients) difference and the Gray-Level (GL) Difference. The third layer is the resultant combination of the two...
Along with the information explosion in the Internet era, the traditional classification methods, such as KNN (k-nearest neighbor), Naive Bayes (NB), encounter bottlenecks due to the endless stream of new words. In this paper, through comparing with the Rocchio and Bayesian algorithms, it has been found that centroid-based algorithms are insufficient for text classification. Therefore, a novel feature...
In many classification problems, there exists additional information which is available during training but not available during testing. In this paper we denote such information as hidden information, and study how to incorporate it to improve the learning performance. Despite its importance, learning with hidden information has not attracted enough attention from the field and existing work in this...
In this paper we present a novel approach to integrate feature similarity and spatial consistency of local features to achieve the goal of localizing an object of interest in an image. The goal is to achieve coherent and accurate labeling of feature points in a simple and effective way. We introduced our Spatial-Visual Label Propagation algorithm to infer the labels of local features in a test image...
We propose a new decision tree model, named the budding tree, where a node can be both a leaf and an internal decision node. Each bud node starts as a leaf node, can then grow children, but then later on, if necessary, its children can be pruned. This contrasts with traditional tree construction algorithms that only grows the tree during the training phase, and prunes it in a separate pruning phase...
This work addresses the problem of creating a Bayesian Network based online semi-supervised handwritten character recognisor, which learns continuously over time to make a adaptable recognisor. The proposed method makes learning possible from a continuous inflow of a potentially unlimited amount of data without the requirement for storage. It highlights the use of unlabelled data for boosting the...
Attribute based image retrieval has offered a powerful way to bridge the gap between low level features and high level semantic concepts. However, existing methods rely on manually pre-labeled queries, limiting their scalability and discriminative power. Moreover, such retrieval systems restrict the users to use only the exact pre-defined query words when describing the intended search targets, and...
This paper focuses on the problem of finding a few representatives for a given dataset, which have both representation and discrimination ability. To solve this problem, we propose a novel algorithm, called Structure Sparsity based Discriminative Representative Selection (SSDRS), to find a representative subset of data points. The selected representative subset keeps the representation ability based...
The Kaczmarz algorithm is popular for iteratively solving an over determined system of linear equations. Randomized version of the Kaczmarz algorithm can converge exponentially and independent of number of equations. Recently an algorithm for finding sparse solution to a linear system of equations has been proposed based on weighted randomized Kaczmarz algorithm. These algorithms solves single measurement...
Sparse representation has attracted great attention in past years. Sparse Representation-based Classification (SRC) algorithm was developed and successfully used in face recognition. However, the importance of sparsity is much emphasized in SRC and the use of collaborative representation (CR) in SRC is ignored. In reality, it is the collaborative representation but not the l1-norm sparsity that makes...
This paper proposes a new robust predictive approach for quality of industrial processes. It draws inspiration from robust AdaBoost for classification and expands to regression tasks. Existing classical AdaBoost for regression (AdaBoost.R2) constructs a strong learner in a stepwise fashion by re-weighting those instances according to their regression results at each iteration. In order to reduce its...
The injection stretch blow moulding (ISBM) has been widely applied in polyethylene terephthalate bottles production process. The modelling of the blowing conditions is important for the control of the process. In this paper, a nonparametric modelling method namely Gaussian Process is employed and applied to estimate the section weights during the process. A key issue in Gaussian Process modelling...
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