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In this paper, we propose a new discriminative dictionary learning framework, called robust Label Embedding Projective Dictionary Learning (LE-PDL), for data classification. LE-PDL can learn a discriminative dictionary and the blockdiagonal representations without using the l0-norm or l1-norm sparsity regularization, since the l0 or l1-norm constraint on the coding coefficients used in the existing...
We study bribery resistance properties in two classes of reputation-based ranking systems, where the rankings are computed by weighting the rates given by users with their reputations. In the first class, the rankings are the result of the aggregation of all the ratings, and all users are provided with the same ranking for each item. In the second class, there is a first step that clusters users by...
A scalable method for mining graph patterns stable under subsampling is proposed. The existing subsample stability and robustness measures are not antimonotonic according to definitions known so far. We study a broader notion of antimonotonicity for graph patterns, so that measures of subsample stability become antimonotonic. Then we propose gSOFIA for mining the most subsample-stable graph patterns...
Opinion mining and demographic attribute inference have many applications in social science. In this paper, we propose models to infer daily joint probabilities of multiple latent attributes from Twitter data, such as political sentiment and demographic attributes. Since it is costly and time-consuming to annotate data for traditional supervised classification, we instead propose scalable Learning...
Since their introduction over a decade ago, time series motifs have become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. In this work we introduce Time Series Chains, which are related to, but distinct from, time series motifs. Informally, time series chains are a temporally ordered set of subsequence patterns, such that each pattern is similar to the pattern...
Time series motifs are approximately repeating patterns in real-valued time series data. They are useful for exploratory data mining and are often used as inputs for various time series clustering, classification, segmentation, rule discovery, and visualization algorithms. Since the introduction of the first motif discovery algorithm for univariate time series in 2002, multiple efforts have been made...
Consider a face image data set from clients of a company and the problem of building a face recognition system from it. Video cameras can be used to acquire several images per client in order to maximize the robustness of the system. However, as the data set grows huge, the accuracy of the system might be seriously compromised since the number of negative samples for each user is increasing. We propose...
To realize Electrocardiography (ECG) signals monitoring systems, compressive sensing (CS) is a new technique to reduce power of biosensors and data transmission. Instead of spending high complexity on reconstructing back to data domain to do signal analysis, compressed analysis (CA) exploits the data structure preserved by CS to directly analyze in the compressed domain. However, compressively-sensed...
Steganography is the science of hiding information to send secret messages using the carrier object known as stego object. Steganographic technology is based on three principles including security, robustness and capacity. In this paper, we present a digital image hidden by using the compressive sensing technology to increase security of stego image based on human visual system features. The results...
Subspace clustering is one of the active research problem associated with high-dimensional data. Here some of the standard techniques are reviewed to investigate existing methodologies. Although, there have been various forms of research techniques evolved recently, they do not completely mitigate the problems pertaining to noise sustainability and optimization of clustering accuracy. Hence, a novel...
In this paper we present our developed and evaluated method for the dynamic mapping of the vertical characteristics inside a building. For achieving that, we extract data from smart-phone sensors and use those data for altitude estimation via the barometric formula. We introduce a novel approach for the extraction of reference pressure during the outdoor-to-indoor-transition of the user inside a building,...
In the background of large data, information security faces more opportunities and challenges. Image steganography as an important means of information hiding, is widely used in military, medical, commercial and other important occasions. Two-dimensional code image as a convenient means of information exchange everywhere. In this paper, an improved LSB information hiding algorithm is designed based...
This paper proposes efficient and powerful deep networks for action prediction from partially observed videos containing temporally incomplete action executions. Different from after-the-fact action recognition, action prediction task requires action labels to be predicted from these partially observed videos. Our approach exploits abundant sequential context information to enrich the feature representations...
Geographical Information System (GIS) is used to collect, manipulate, analyze, and display the geospatial data. The compilation and management of this spatial data is expensive and time consuming task. Due to rapid growth of distributed networks and Internet, it becomes easy to handle data but at the same time it becomes easy to copy or distribute the spatial data. Therefore copyright protection,...
In this paper, a new heterogeneous neural networks based deep learning method, named HNNDL, is presented for supervised classification of hyperspectral image (HSI) with a small number of labeled samples. Specifically, a deep neural Network (DNN) and a convolutional neural network (CNN) are combined to build a HNNDL architecture. The proposed architecture contains three modules: 1) dimension reduction...
As data mining grows its significance in real world information extraction, we need a robust methodology without using rich assumptions. In detection of irregularities, statistical methods are very successful when we assume some distribution. However, even when we encounter data with unknown distribution, we must first assume some distribution, which may cause incorrect inference. This paper applies...
Digitalisation of industrial processes, also called the fourth industrial revolution, is leading to availability of large volume of data containing measurements of many process variables. This offers new opportunities to gain deeper insights on process variability and its effects on quality and performance. Manufacturing facilities already use data driven approaches to study process variability and...
With the arrival of big data era, data mining techniques have been widely used to build models for cyber security applications such as spam filtering, malware or virus detection, and intrusion detection. This project proposes a novel approach that uses randomness to improve robustness of data mining models used in cyber security applications against attacks that try to evade detection by adapting...
Identifying the physical person behind an SNS account has become a critical issue in investigations of SNS-involved crime cases. It is a challenging task because information provided by users on an SNS platform could be false, conflicting, missing and deceptive. One way to gain an accurate profile of a user is to link up all their multiple accounts created on different social platforms, which is referred...
We present a new algorithm for discovering clusters in noisy data streams using dynamic and cluster-specific temporal decay factors. Our improvement helps identify and adapt to evolving trends by adapting the weighting of stream data based on both content attributes and temporal arrival patterns. Our experimental results show that the proposed algorithm can discover better quality clusters in noisy...
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