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Domain knowledge and feature representation
2016 4th International Winter Conference on Brain-Computer Interface (BCI), 2016Identifying covert internal brain by their expression in neural images, particularly from magnetic resonance imaging, is a popular, powerful, and important area of research whose ultimate expression is known now as “brain reading.” The nature of the imaging data is challenging however, in that they typically have two orders of magnitude more features ...
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Coupled-Feature Hypergraph Representation for Feature Selection
2015Real-world objects and their features tend to exhibit multiple relationships rather than simple pairwise ones, and as a result basic graph representation can lead to substantial loss of information. Hypergraph representations, on the other hand, allow vertices to be multiply connected by hyperedges and can hence capture multiple or higher order ...
Zhihong Zhang 0001 +3 more
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A Representation Method for Machining Features
1992Abstract Features are abstraction ideas from application viewpoints. For example, the Design Feature is the shape abstraction from the design view, and the Machining Feature is one from the machining operation view. Especially the Machining Feature is a very important idea for connecting the product model and the machining operations.
Fumiki Tanaka, Takeshi Kishinami
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A representation formalism for feature-based design
Computer-Aided Design, 1996Feature-based design is regarded as a promising approach for design. However, it lacks a formal methodology for system development and operation. One repercussion of this is that feature-based design is, at present, implemented in a relatively ad hoc manner.
Kim Chongsu, Peter J. O'Grady
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Distributed Feature Representations for Dependency Parsing
IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2015This paper presents an approach to automatically learning distributed representations for features to address the feature sparseness problem for dependency parsing. Borrowing terminologies from word embeddings, we call the feature representation feature embeddings.
Wenliang Chen +2 more
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Feature Extraction and Representation
2018This chapter is focused on some classical feature representations for image and video analysis. In particular, we will introduce the histogram-based features, texture features, and some local point features.
Shengrong Gong +5 more
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Geometry representations with unsupervised feature learning
2016 International Conference on Big Data and Smart Computing (BigComp), 2016Geometry data in massive amounts can be generated thanks to the modern capture devices and mature geometry modeling tools. It is essential to develop the tools to analyze and utilize this big data. In this paper, we present an exploration of analyzing geometries via learning local geometry features.
Yeo-Jin Yoon +5 more
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Texture feature representation in dynamic environments
2010 International Conference on High Performance Computing & Simulation, 2010This paper presents a novel approach to detect and formulate time varying changes in texture content on a sequence of images registered from a scene by using Gabor wavelets representation. The changes might occur under variable illumination or different location of viewpoint for image registration.
Kyeong Deok Woo +2 more
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Feature Extraction: Issues, New Features, and Symbolic Representation
1999Feature extraction is an important part of object model acquisition and object recognition systems. Global features describing properties of whole objects,or local features denoting the constituent parts of objects and their relationships may be used. When a model acquisition or object recognition system requires symbolic input,the features should be ...
Maziar Palhang, Arcot Sowmya
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Feature Representation and Feature Matching for Heterogeneous Defect Prediction
2019Software Defect Prediction (SDP) is one of the highly influential software engineering research topics. Early within-project defect prediction (WPDP) used intra-project data. However, it has limitations in prediction efficiency for new projects and projects without adequate training data.
Thae Hsu Hsu Mon, Hnin Min Oo
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