Isomap

수학노트

둘러보기로 가기 검색하러 가기

노트

The most time-consuming step in Isomap is to compute the shortest paths between all pairs of data points based on a neighbourhood graph.^[1]
The classical Isomap (C-Isomap) is very slow, due to the use of Floyd’s algorithm to compute the shortest paths.^[1]
The purpose of this paper is to speed up Isomap.^[1]
Next, the proposed approach is applied to two image data sets and achieved improved performances over standard Isomap.^[2]
: S-ISOMAP is a manifold learning algorithm, which is a supervised variant of ISOMAP.^[3]
Experiment results show that 3N-Isomap is a more practical and simple algorithm than Isomap.^[4]
A SemiSupervised local multimanifold Isomap by linear embedding is proposed.^[5]
We mainly evaluate SSMM-Isomap for manifold feature learning, data clustering and classification.^[5]
It is becoming more and more difficult for MDS and ISOMAP to solve the full eigenvector problem with the increasing sample size.^[6]
The basic idea: In MDS and ISOMAP, solving the full eigenvector problem leads to higher complexity.^[6]
If MDS and ISOMAP also solve a sparse eigenvector problem, its execution will greatly speed up.^[6]
For ISOMAP, we computed the time of solving the full NxN eigenvector problem.^[6]
the result of ISOMAP is showed below, which indicates the successful extraction of the underlying manifold in original 3D space.^[7]
Isomap can be viewed as an extension of Multi-dimensional Scaling (MDS) or Kernel PCA.^[8]
Isomap seeks a lower-dimensional embedding which maintains geodesic distances between all points.^[8]
The algorithm can be selected by the user with the path_method keyword of Isomap .^[8]
The eigensolver can be specified by the user with the path_method keyword of Isomap .^[8]
To give you a solution first, you can use eigen_solver='dense' when using Isomap .^[9]
No, there is currently no way to set a seed for KernelPCA from Isomap .^[9]
L-Isomap reduces both the time and space complexities of Isomap significantly.^[10]
So far, we have presented EL-Isomap through comparing with L-Isomap (and Isomap) in various viewpoints.^[10]
The D embeddings of the data set were calculated by LLE, Laplacian eigenmap, Isomap, L-Isomap, and EL-Isomap.^[10]
The neighborhood sizes of LLE, Laplacian eigenmap, Isomap, L-Isomap, and EL-Isomap are set as , , , , and , respectively.^[10]
Isomap is distinguished by its use of the geodesic distance induced by a neighborhood graph embedded in the classical scaling.^[11]
In a similar manner, the geodesic distance matrix in Isomap can be viewed as a kernel matrix.^[11]
Isomap can be performed with the object Isomap .^[12]
The eigensolver can be specified by the user with the eigen_solver keyword of Isomap .^[12]
Isomap is viewed as a variant of metric multidimensional scaling (MDS) to model nonlinear data using its geodesic distance.^[13]
No such type of method has been used in recent years from the Isomap perspective.^[13]
Second section provides an overview and background work, which is followed by a brief discussion on manifold learning and Isomap.^[13]
Isomap stands for isometric mapping.^[14]
Isomap starts by creating a neighborhood network.^[14]
Isomap uses the above principle to create a similarity matrix for eigenvalue decomposition.^[14]
Classical MDS uses the euclidean distances as the similarity metric while isomap uses geodesic distances.^[14]
We will use the neighborhood graph to achieve ISOMAP.^[15]
kNN(k-Nearest Neighbors), the most common choice for ISOMAP, connects each data point to its k nearest neighbors.^[15]
It uses ISOMAP and it embeds 4096 pixels and 698 face pictures into 2D space and lines them onto light direction and up-down pose.^[15]

소스

메타데이터

위키데이터

ID : Q6086067

Spacy 패턴 목록

[{'LEMMA': 'Isomap'}]

원본 주소 "https://wiki.mathnt.net/index.php?title=Isomap&oldid=51607"