Isomap
노트
- Isomap is distinguished by its use of the geodesic distance induced by a neighborhood graph embedded in the classical scaling.[1]
- In a similar manner, the geodesic distance matrix in Isomap can be viewed as a kernel matrix.[1]
- Isomap can be viewed as an extension of Multi-dimensional Scaling (MDS) or Kernel PCA.[2]
- Isomap seeks a lower-dimensional embedding which maintains geodesic distances between all points.[2]
- Isomap can be performed with the object Isomap .[2]
- The algorithm can be selected by the user with the path_method keyword of Isomap .[2]
- Isomap is viewed as a variant of metric multidimensional scaling (MDS) to model nonlinear data using its geodesic distance.[3]
- No such type of method has been used in recent years from the Isomap perspective.[3]
- Second section provides an overview and background work, which is followed by a brief discussion on manifold learning and Isomap.[3]
- Isomap stands for isometric mapping.[4]
- Isomap starts by creating a neighborhood network.[4]
- Isomap uses the above principle to create a similarity matrix for eigenvalue decomposition.[4]
- Classical MDS uses the euclidean distances as the similarity metric while isomap uses geodesic distances.[4]
- We will use the neighborhood graph to achieve ISOMAP.[5]
- kNN(k-Nearest Neighbors), the most common choice for ISOMAP, connects each data point to its k nearest neighbors.[5]
- 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.[5]