Sentence examples for manifold embedding methods from inspiring English sources

These approaches include multi-detector methods, manifold embedding methods, and non-linear regression methods.

Manifold embedding methods such as PCA, KPCA, LDA, and kernel discriminant analysis have been used to extract texture features, and these features are used to estimate the discrete head pose [26 29].

The repre-sentative methods of this type include the manifold embedding method, the flexible-model-based method, and the machine-learning-based method.

Manifold embedding based methods seek low-dimensional manifolds that model the continuous variation in head pose.

To overcome this drawback, we propose a new dimensionality reduction method, called sparse manifold embedding (SME), based on graph embedding and sparse representation for hyperspectral image (HSI).

Furthermore, we try to make full use of the prior label information to design a novel supervised learning method termed sparse discriminant manifold embedding (SDME).

Protocol for Petri dish sized embedding method.

flexible manifold embedding.

In this paper, a framework is proposed for a supervised form of manifold learning called biased manifold embedding.

As explained in the Introduction, manifold embedding algorithms can be easily adopted for network embedding, although not all algorithms that learn manifolds are applicable for this task: only those able to embed a topology starting from a distance or adjacency matrix can be used.

We chose to compare MCE, ncMCE and Isomap (and Isomap + FSW) against well-established unsupervised and supervisedly tuned manifold embedding algorithms that accept a distance or adjacency matrix as input.