Sentence examples for multiple feature spaces from inspiring English sources

First, the proposed method offers multiple feature spaces which are designed from GPS data, localization prediction model, image holistic features, and image local features.

Afterwards, we propose a novel method called K-Nearest Neighbors from Multiple Feature Spaces (KNN-MFS) to fuse these candidate sets.

Unlike the traditional Multiple Kernel Learning (MKL) with the implicit kernels, Multiple Empirical Kernel Learning (MEKL) explicitly maps the original data space into multiple feature spaces via different empirical kernels.

Hinrichs et al. [144, 145] and Zhang et al. [4] recently proposed the multi-kernel support vector machine (MK-SVM) algorithm, which is based on multi-kernel learning and extends the kernel tricks in SVM to the multiple feature spaces.

With a focus on the subjects, the feature embedding methods, such as multi-view spectral embedding (MSE) [139] and multi-view local linear embedding (MLLE) [140], have been used to explore the geometric structures of local patches in multiple feature spaces and align the local patches in a unified feature space with maximum preservation of the geometric relationships.

The core of the segmenter is a parallel version of the mean shift algorithm that works simultaneously on multiple feature space kernels.

The MKELM method is subsequently developed by integrating these two types of kernels with a multi-kernel learning strategy, which can effectively explore the supplementary information from multiple nonlinear feature spaces for more robust classification of EEG.

The proposed MREKLM adopts the random projection idea to map samples into multiple low-dimensional empirical feature spaces with lower computational complexity O(MP3), where P ≪N) is the number of the randomly selected samples.

We call the space that merely contains the K extracted genes the feature space from which multiple feature subspaces can be generated.

The two key differences between the methods in [31] and [32] and the LRSMs are (1) the LRSMs are able to adequately handle multiple features, drawn from different feature spaces, and (2) the least squares loss function is employed instead of hinge loss, resulting into formulation (4) which can be efficiently solved for large-scale data.

It is more reasonable to assume that multiple feature vectors live in a union of feature spaces, which is what the proposed method actually does in a principled way.