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Credit risk analysis seeks to determine whether a customer is likely to default on the financial obligation, which is a very important problem in finance. In this paper, we will present a machine learning framework to deal with this problem by formulating it as a binary classification problem. The framework consists of two parts: dictionary learning and classifier training. Firstly, we introduce a...
In this paper, three new algorithms are presented by applying group idea and collaborative thought to projective dictionary pair learning (DPL). These algorithms further extend the framework of discriminative dictionary learning (DL). Based on projective dictionary pair learning which realizes the goals of signal representation and pattern classification by learning a synthesis dictionary and an analysis...
Clustering, the process of grouping unlabelled data, is an important task in data analysis. It is regarded as one of the most difficult tasks due to the large search space that must be explored. Feature selection is commonly used to reduce the size of a search space, and evolutionary computation (EC) is a group of techniques which are known to give good solutions to difficult problems such as clustering...
We propose a novel computationally efficient hierarchical dictionary learning (HDL) approach for data-driven unmixing and functional connectivity analysis of functional magnetic resonance imaging (fMRI) data. It is shown that by simultaneously exploiting the sparsity of the spatial brain maps and the incoherence among their evolution in time or task functions, one can achieve better performance while...
Support Vector Machines (SVM) are a family of algorithms that are used in classification and regression tasks. Often, multiple SVMs are combined in a coding scheme to provide multi-class classification capabilities. Generally, multi-class classification systems are evaluated on their accuracy of producing a correct coding by using test data and successful predictions are counted as a percentage of...
The High Efficiency Video Coding (HEVC) adopts a hierarchical quad-tree based coding unit (CU) partitioning structure, which allows to recursively split a block into four equally sized blocks. At each depth level, there are up to 35 intra prediction modes. Therefore, enormous computational complexity is introduced due to the recursive Rate-Distortion Optimization (RDO) process on all possible depth...
The real-time requirements of hardwired HEVC encoder demand that, at the grain of coding tree unit (CTU), the maximum computation should be reduced by a fast CU mode decision algorithm. In addition, to realize the parallel rate-distortion optimization (RDO) of different CU modes, the current CU mode decision should not use the auxiliary information from other CU modes. Considering the above constraints,...
Recently, several tree-structured vector quantizers had been proposed. But almost all trees used are binary trees and hence the training samples contained in each node are forced to be divided into two clusters artificially. We present a general-tree-structured vector quantizer that is based on a genetic clustering algorithm. This genetic clustering algorithm can divide the training samples contained...
Determining the best partitioning structure for a CTU is a time consuming operation for the HEVC encoder. This paper presents a fast CU size selection algorithm for HEVC using a CU classification technique. The proposed algorithm achieves an average of 67.83% encoding time efficiency improvement with a negligible rate-distortion loss.
This paper proposes a new Double Coding Local Binary Pattern algorithm (d-LBP) to improve the weaknesses of traditional LBP algorithm. Firstly, it defines two thresholds: the amplitude threshold and the difference threshold, which succeed in taking full consideration of the relationship among pixel gray values and reducing sampling points. Secondly, the paper uses the d-LBP algorithm to extract statistical...
Feature selection (FS) and classifier design (CD) are two basic stages in the construction of a classification system. Typically, both tasks have been studied separately in literature. FS aims to remove irrelevant and redundant features whereas CD generates a prediction rule for classifying patterns whose class is unknown. Despite the relationship between FS and CD with radial basis function networks...
Nonlinear dimensionality reduction (DR) is a basic problem in manifold learning. However, many DR algorithms cannot deal with the out-of-sample extension problem and thus cannot be used in large-scale DR problem. Furthermore, many DR algorithms only consider how to reduce the dimensionality but seldom involve with how to reconstruct the original high dimensional data from the low dimensional embeddings...
We apply our recently developed concept of mutual exclusivity [1] in the context of discriminative coding, to the problem of learning dictionary for representing signals drawn from N classes in a way that optimizes their discriminability. We first briefly review our mutual-exclusivity concept and then deploy it a simple discriminative dictionary learning algorithm that directly generalizes the well-known...
Codebook plays an important role in the bag-of-visual-words (BoW) model for image classification. However, the traditional codebook generation procedure ignores the spatial information. Although a lot of works have been done to consider the spatial information for codebook generation, most of them rely on fixed region selection or partition of images, hence are not able to cope with the variations...
Recently, background modeling (shortly BgModeling) plays a more and more important role in high-efficiency surveillance video coding. Meanwhile, many practical video coding applications also present some specific requirements for BgModeling, such as the low memory cost and low computational complexity. However, existing BgModeling methods are mostly designed for video content analysis such as object...
Recently, dictionary learned by sparse coding has been widely adopted in image classification and has achieved competitive performance. Sparse coding is capable of reducing the reconstruction error in transforming low-level descriptors into compact mid-level features. Nevertheless, dictionary learned by sparse coding does not have the ability to distinguish different classes. That is to say, it is...
A common method to solve a multiclass classification problem is to reduce the problem to a serial binary classification problems and combine them via Error-Correcting Output Codes (ECOC). The ECOC contains three parts: coding design, decoding algorithm, and base dichotomizer. Recently, the Loss-Weighted (LW) decoding algorithm (Escalera et al., PAMI2010), which introduces a weight matrix to the Loss-Based...
This paper proposes a novel algorithm of neural network structure evolve. First, the algorithm designs an indirect encoding schema representing the structure of neural network, use joint seed representing the existence of connection in neural network. Then, creating and evolving the coordinates of the joint seed using Binary Quantum-behaved Particle Swarm Optimization (BQPSO), evolving the value of...
Protocol-independent redundant traffic elimination (RTE) is an "on the fly" method for detecting and removing redundant chunks of data from network-layer packets traversing a constrained link or path. Efficient algorithms are needed to sample data chunks and detect redundancy, so that RTE does not hinder network throughput. A recently proposed static algorithm samples chunks based on highly-redundant...
According to the abnormal state detection without enough priori knowledge and fault samples, a new detector generation method with step radiuses is proposed. The method separates abnormal state space into several regions, and then sets different detector radius for them according to actual situations, has great flexibility and higher coverage rate to space. Artificial immune real-valued negative selection...
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