Sentence examples for a joint feature from inspiring English sources
The phrase "a joint feature" is correct and usable in written English
It can be used to describe a characteristic or aspect that is shared or common between two or more entities. Example: "The new software update includes a joint feature that enhances collaboration between users across different platforms."
In this context, two open-access, rapid-publication journals from The Optical Society of America OSAA), Optical Materials Express and Biomedical Optics Express, will publish a joint feature issue covering advances in biophotonics materials.
In this paper, we propose a joint feature distribution (JFD) principle to design novel discriminative features which could be the joint distribution of features on adjacent positions or the joint distribution of different features on the same location.
A joint feature for the majority of these papers is that they study the operators from or to Bloch-type or Bergman-type spaces.
Considering the different features of OFDM and SCLD in two domains, we define a joint feature function to widen the distinguished gap.
We propose a joint feature utilization framework where local shape-motion descriptors obtained from contemporary feature detectors were supplemented by spatio-temporal extensions of global texture descriptors.
In [3], authors propose a joint dictionary learning mechanism for learning HR and LR dictionaries in a joint feature space, thus enforcing the similarity between HR and LR sparse coefficients.
Specifically, we first introduce a joint feature-sample selection (JFSS) method for selecting an optimal subset of samples and features, to learn a reliable diagnosis model.
In this paper, we introduce l1-norm driven sparse representation into feature selection, and propose a novel joint feature weights learning algorithm, named sparse discriminative feature weights (SDFW).
(2) we design a set of joint feature representations for curve-path which describes different characteristics of a curve and its corresponding path.
This method utilizes Canonical Correlation Analysis (CCA) [45] for deriving a domain-invariant joint feature space to associate data from the source and target.
We apply these steps to MALDI spectral data collected from normal and tumor lung tissue samples, and then compare the performance of feature selection with FDR followed by classification with an SVM, versus joint feature selection and classification with Bayesian sparse multinomial logistic regression (SMLR).
