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Face recognition based on sparse representation is investigated in this paper. Dimensionality reduction is the process of projecting original image data into a low dimensional space, and usually be conducted before dictionary learning. However dimensionality reduction may lose information that is important to the face recognition task. Although the accuracy rate of face recognition varies with different...
In this paper, a novel unsupervised method for learning sparse features combined with support vector machines for classification is proposed. The classical SVM method has restrictions on the large-scale applications. This model uses sparse auto encoder, a deep learning algorithm, to improve the performance. Firstly, we use multiple layers of sparse auto encoder to learn the features of the data. Secondly,...
In recent years, plenty of advanced approaches for universal JPEG image steganalysis have been proposed due to the need of commercial and national security. Recently, a novel sparse-representation-based method was proposed, which applied sparse coding to image steganalysis [4]. Despite satisfying experimental results, the method emphasized too much on the role of l1-norm sparsity, while the effort...
In this paper, a novel general robust SVM approach for classification is proposed which can better characterize the distribution of the data compared with the traditional SVM. The classical Support Vector Machines is heavily relied on the Support Vector which has neglected the holistic distribution of the data that sometimes will lead to gross errors. We first take use of the majority of the data...
This paper focuses on the task of text sentiment analysis in hybrid online articles and web pages. Traditional approaches of text sentiment analysis typically work at a particular level, such as phrase, sentence or document level, which might not be suitable for the documents with too few or too many words. Considering every level analysis has its own advantages, we expect that a combination model...
Automatic target recognition (ATR) is an important task in image application. This paper concentrates on two key subroutines of ATR system: Pre-treatment and design of classifier. In the pre-treatment subroutine, a new method based on Rough Set (RS) is proposed to partition the original sample set into some subsets and calculate their class membership, so that some samples can be chosen by class membership...
Blog Distillation is the process of finding a blog with a principle and recurring interest. In this paper, two baselines are used to validate the results of our experiments. A set of features of individual feed is firstly constructed by decision tree to represent the similarity distribution of every feed against certain interest. Features are then selected by computing their centroid distances to...
Nowadays people realize that it is difficult to find information simply and quickly on the bulletin boards. In order to solve this problem, people propose the concept of bulletin board search engine. This paper describes the priscrawler system, a subsystem of the bulletin board search engine, which can automatically crawl and add the relevance to the classified attachments of the bulletin board. Priscrawler...
EM (expectation-maximization) algorithm is a classical method for parameter estimation of HMM (Hidden Markov model ). Concerning that EM algorithm is easily affected by initial parameter values, we proposed a mixture splitting algorithm based on decision boundary confusion (DBC) to describe more about boundary distribution. The algorithm mainly includes three aspects: firstly the number of incremented...
This paper is to introduce a novel semi-supervised learning algorithm named linear neighborhood spread (LNS), which is capable for learning manifold structures. Labeled and unlabeled data are represented as vertices in a weighted graph, and each data point is assumed can be linearly constructed from its neighborhood. Labels are spread through the edges, and the weighted graph is regarded as probabilistic...
Collaborative filtering has been very successful in both research and applications. The K-nearest neighbor (KNN) method is a popular way for its realizations. Its key technique is to find k nearest neighbors for a given user to predict his interests. User-based clustering algorithms of collaborative filtering classify the users into some clusters and select top-N neighbors by using all items to compute...
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