Sentence examples for manifold learning methods from inspiring English sources

The effectiveness and superiorities of the proposed method are demonstrated through several comparative experiments with other three manifold learning methods.

The experimental results validate that the SDMP is more effective than the other three manifold learning methods for implementation bearing fault diagnosis, and it is more robust when deal with noise interference signal.

Different from traditional manifold learning methods, which construct an adjacency graph from K-nearest neighbors or ɛ-ball method, the SRPE algorithm constructs the adjacency graph by solving a robust sparse representation problem through convex optimization.

Traditional manifold learning methods focus on preserving only the intra-class geometric properties of the manifold embedded in the high-dimensional ambient space, so they cannot fully utilize the underlying discriminative knowledge of the data.

To capture the local structure information, several manifold learning methods have been proposed.

In this paper, we apply manifold learning methods, in particular ISOMAP and Laplacian Eigenmap, to examine the underlying geometric structure of whale vocalizations.

Aiming at the flaws encountered by the kurtogram method, this paper proposes a novel kurtogram manifold learning method for signal de-noising in whole time-frequency space.

Second, the proposed manifold learning method, NFLE, preserves the local structures among samples in manifold distributions.

Chang et al. [3] proposed a SR method based on this principle using the manifold learning method of locally linear embedding (LLE) [24].

The NFLE transformation [18] was a new manifold learning method based on the point-to-line (p-2-l) distance measurement which was originated from the nearest linear combination (NLC) approach [28].

An improved manifold learning method, called enhanced semi-supervised local Fisher discriminant analysis (ESELF), for face recognition is proposed.