Weighted line fitting algorithms for mobile robot map building and efficient data representation

2004 ◽  
Author(s):  
S.T. Pfister ◽  
S.I. Roumeliotis ◽  
J.W. Burdick
Keyword(s):  
Mobile Robot ◽  
Data Representation ◽  
Map Building ◽  
Efficient Data ◽  
Line Fitting

Auto-weighted concept factorization for joint feature map and data representation learning

2021 ◽  
pp. 1-13
Author(s):  
Yikai Zhang ◽  
Yong Peng ◽  
Hongyu Bian ◽  
Yuan Ge ◽  
Feiwei Qin ◽  
...  
Keyword(s):  
Objective Function ◽  
Optimization Procedure ◽  
Feature Space ◽  
Representation Learning ◽  
Data Representation ◽  
Data Sets ◽  
Reconstruction Process ◽  
Factorization Model ◽  
Efficient Data ◽  
Concept Factorization

Concept factorization (CF) is an effective matrix factorization model which has been widely used in many applications. In CF, the linear combination of data points serves as the dictionary based on which CF can be performed in both the original feature space as well as the reproducible kernel Hilbert space (RKHS). The conventional CF treats each dimension of the feature vector equally during the data reconstruction process, which might violate the common sense that different features have different discriminative abilities and therefore contribute differently in pattern recognition. In this paper, we introduce an auto-weighting variable into the conventional CF objective function to adaptively learn the corresponding contributions of different features and propose a new model termed Auto-Weighted Concept Factorization (AWCF). In AWCF, on one hand, the feature importance can be quantitatively measured by the auto-weighting variable in which the features with better discriminative abilities are assigned larger weights; on the other hand, we can obtain more efficient data representation to depict its semantic information. The detailed optimization procedure to AWCF objective function is derived whose complexity and convergence are also analyzed. Experiments are conducted on both synthetic and representative benchmark data sets and the clustering results demonstrate the effectiveness of AWCF in comparison with the related models.

Breakthroughs and Limitations of XML Grammar Similarity

2009 ◽  
pp. 140-148 ◽  
Author(s):  
Joe Tekli
Keyword(s):  
Web Applications ◽  
Structural Similarity ◽  
Data Representation ◽  
Multimedia Data ◽  
Xml Documents ◽  
Efficient Data ◽  
Unified View ◽  
Data Layer ◽  
Query Systems ◽  
Information Interchange

W3C’s XML (eXtensible Mark-up Language) has recently gained unparalleled importance as a fundamental standard for efficient data management and exchange. The use of XML covers data representation and storage, database information interchange, data filtering, as well as Web applications interaction and interoperability. XML has been intensively exploited in the multimedia field as an effective and standard means for indexing, storing, and retrieving complex multimedia objects. SVG1, SMIL2, X3D3 and MPEG-74 are only some examples of XML-based multimedia data representations. With the ever-increasing Web exploitation of XML, there is an emergent need to automatically process XML documents and grammars for similarity classification and clustering, information extraction, and search functions. All these applications require some notion of structural similarity, XML representing semi-structured data. In this area, most work has focused on estimating similarity between XML documents (i.e., data layer). Nonetheless, few efforts have been dedicated to comparing XML grammars (i.e., type layer). Computing the structural similarity between XML documents is relevant in several scenarios such as change management (Chawathe, Rajaraman, Garcia- Molina, & Widom, 1996; Cobéna, Abiteboul, & Marian, 2002), XML structural query systems (finding and ranking results according to their similarity) (Schlieder, 2001; Zhang, Li, Cao, & Zhu, 2003) as well as the structural clustering of XML documents gathered from the Web (Dalamagas, Cheng, Winkel, & Sellis, 2006; Nierman & Jagadish, 2002). On the other hand, estimating similarity between XML grammars is useful for data integration purposes, in particular the integration of DTDs/schemas that contain nearly or exactly the same information but are constructed using different structures (Doan, Domingos, & Halevy, 2001; Melnik, Garcia-Molina, & Rahm, 2002). It is also exploited in data warehousing (mapping data sources to warehouse schemas) as well as XML data maintenance and schema evolution where we need to detect differences/updates between different versions of a given grammar/schema to consequently revalidate corresponding XML documents (Rahm & Bernstein, 2001). The goal of this article is to briefly review XML grammar structural similarity approaches. Here, we provide a unified view of the problem, assessing the different aspects and techniques related to XML grammar comparison. The remainder of this article is organized as follows. The second section presents an overview of XML grammar similarity, otherwise known as XML schema matching. The third section reviews the state of the art in XML grammar comparison methods. The fourth section discusses the main criterions characterizing the effectiveness of XML grammar similarity approaches. Conclusions and current research directions are covered in the last section.

Sign in / Sign up

Export Citation Format

Share Document