Recently proposed algorithms for nonlinear dimensionality reduction fall broadly into two categories which have different advantages and disadvantages: global (Isomap [1]), and local (Locally Linear Embedding [2], Laplacian Eigenmaps [3]). We present two variants of Isomap which combine the advantages of the global approach with what have previously been exclusive advantages of local methods: c...

Isomap is a classic and representative manifold learning algorithm for nonlinear dimensionality reduction, which aims to circumvent the problem of “the curse of dimensionality” and attempts to recover the intrinsic structure hidden in high-dimensional data based on the assumption that data lie in or near a single manifold. However, Isomap fails to work when data set consists of multi-clusters o...

As one of the most promising nonlinear dimensionality reduction techniques, Isometric Mapping (ISOMAP) performs well only when the data belong to a single well-sampled manifold, where geodesic distances can be well approximated by the corresponding shortest path distances in a suitable neighborhood graph. Unfortunately, the approximation gets less and less precise generally as the number of edg...

Sistem pengenalan wajah merupakan sistem yang dapat mengenali seseorang dengan bantuan komputer. Untuk tersebut, dilakukan ekstraksi fitur terlebih dahulu. Pada penelitian ini digunakan metode isomap untuk mengekstrak wajah. Isomap suatu mengubah dimensi citra berdimensi tinggi menjadi fitur-fitur memiliki rendah. Data adalah diperoleh dari 6 orang, setiap orang 4 variasi ekspresi citra. Setela...

According to the nonlinear characteristic of the speech signal, this paper presents a novel robust MFCC extraction method using sample-ISOMAP. ISOMAP is a nonlinear dimensionality reduction method based on the theory of manifold, it can reveal the meaningful low-dimensional structure hidden in the high-dimensional observations. In the proposed method, ISOMAP is first applied for calculating the...

Dimensionality reduction methods have shown their usefulness for both supervised and unsupervised tasks in a wide range of application domains. Several linear and nonlinear approaches have been proposed in order to derive meaningful low-dimensional representations of high-dimensional data. Among nonlinear algorithms manifold learning methods, such as isometric feature mapping (Isomap), have rec...

In this paper, we present a new visual clustering algorithm inspired by nonlinear dimension reduction technique: Isomap. The algorithm firstly defines a new graph distance between any two nodes in complex networks and then applies the distance matrix to Isomap and projects all nodes into a two dimensional plane. The experiments prove that the projected nodes emerge clear clustering property whi...

Isometric feature mapping (ISOMAP) is a nonlinear dimensionality reduction method used for extracting features from spatiotemporal data. The traditional principal component analysis (PCA), linear method, measures the distance between two data points based on Euclidean (line segment), which cannot reflect actual in space. By contrast, ISOMAP geodesic distance, more closely reflects by view of tr...