http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Sparse Polynomial Mapping for Manifold Learning
Ying Xia,Qiang Lu,Hae-Young Bae 보안공학연구지원센터(IJSIP) 2014 International Journal of Signal Processing, Image Vol.7 No.6
Manifold learning is an approach for nonlinear dimensionality reduction and has been a hot research topic in the field of computer science. A disadvantage of manifold learning methods is, however, that there are no explicit mappings from the high-dimensional feature space to the low-dimensional representation space. It restricts the application of manifold learning methods in many practical problems such as target detection and classification. Previously, some methods have been proposed to provide linear or nonlinear mappings for manifold learning methods. However, a disadvantage of all these methods is that the learned projective functions are combinations of all the original features, thus it is often difficult to interpret the results. Moreover, the dense projection matrices of these approaches lead to a high cost of computation and storage. In this paper, a sparse polynomial mapping approach is proposed for manifold learning. We first get the low-dimensional representations of the high-dimensional input data by using a manifold learning method, and then a 1-based simplified polynomial regression is used to get a sparse polynomial mapping between the high-dimensional data and their low-dimensional representations. In particular, we apply this to the method of Laplacian eigenmap and derive a sparse nonlinear manifold learning algorithm, which is named sparse locality preserving polynomial embedding. Experimental results on real-world data show the effectiveness of our approach.
Cross-Media Retrieval using Probabilistic Model of Automatic Image Annotation
Ying Xia,YunLong Wu,JiangFan Feng 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.4
In recent years, automatic image annotation (AIA) has been applied to cross-media retrieval usually due to its advantage of mining correlations of images and annotation texts efficiently. However, some AIA methods just annotate images as a unit and the accuracy of annotation may not be acceptable. In this paper, we propose a kind of probabilistic model which may assign keywords to an un-annotated image automatically based on a training dataset of images. Images in the training dataset are segmented into regions and a kind of vocabulary called blob is used to represent these image regions. Blobs are generated by using K-Means algorithm to cluster these image regions. Through this model, we can predict the probability of assigning a keyword into a blob. After the accomplishment of annotation, a keyword corresponds to one image region. Furthermore, the feature vectors of text documents are generated by TF.IDF method and images’ automatic annotation information is used to retrieve relevant text documents. Experiments on the IAPR TC-12 dataset and 500 Wikipedia webpages about landscape show the usefulness of applying probabilistic model of AIA to the cross-media retrieval.
Simplified Predicate Locking Scheme for Concurrency Control on R - tree
Ying Xia,Kee-Wook Rim,Jae-Dong Lee,Hae-Young Bae 한국정보과학회 2001 한국정보과학회 학술발표논문집 Vol.28 No.1B
Despite extensive research on R-tree, most of the proposed schemes have not been integrated into existing DBMS due to the lack of protocol to provide consistency in concurrent environment. R-link tree is an acceptable data structure to deal with this issue, but still not enough. In this paper, we focus on a simplified predicate locking mechanism based on R-link tree for concurrency control and phantom protection. An in-memory operation control list (OCList) used to suspend some conflicting operations is designed here. The main features of this approach are (1) it can be implemented easily and do not need any extra information. (2) Only-one-lock is held when descending R-tree even when node split happens, while lock-coupling scheme is performed when ascending. No deadlocks are possible. (3) Searches and insertions are not unnecessarily restricted. (4) Insert and Delete phantom in R-link tree are avoided through beforehand predication.