Isomap is a highly popular manifold learning and dimensionality reduction technique that effectively performs multidimensional scaling on estimates of geodesic distances. However, the resulting output is extremely sensitive to parameters that control the selection of neighbors at each point. To date, no principled way of setting these parameters has been proposed, and in practice they are often...

a r t i c l e i n f o a b s t r a c t Manifold learning has become a hot issue in the field of machine learning and data mining. There are some algorithms proposed to extract the intrinsic characteristics of different type of high-dimensional data by performing nonlinear dimensionality reduction, such as ISOMAP, LLE and so on. Most of these algorithms operate in a batch mode and cannot be effec...

The bidirectional gated recurrent unit (BiGRU) method based on dissolved gas analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings such as fuzzy boundaries DGA data, and BiGRU parameters difficult to determine. Therefore, this paper proposes a diagnosis landmark isometric mapping (L-Isomap) Improved Sand Cat Swarm Optimizat...

ARE is a non-linear dimensionality reduction technique for embedding observation trajectories, which captures state dynamics that traditional methods do not. The core of ARE is a semidefinite optimization with constraints requiring actions to be distance-preserving in the resulting embedding. Unfortunately, these constraints are quadratic in number and non-local (making recent scaling tricks in...

This paper investigates approaches for low dimensional speech feature transformation using manifold learning. It has recently been shown that speech sounds may exist on a low dimensional manifold nonlinearly embedded in high dimensional space. A number of techniques have been developed in recent years that attempt to discover the geometric structure of the underlying low dimensional manifold. T...

Manifold learning based methods have been widely used for non-linear dimensionality reduction (NLDR). However, in many practical settings, the need to process streaming data is a challenge for such methods, owing to the high computational complexity involved. Moreover, most methods operate under the assumption that the input data is sampled from a single manifold, embedded in a high dimensional...

Transformers play a crucial role in power systems. In this respect, fault diagnosis and aging state assessment have garnered significant attention from researchers. Herein, accelerated thermal Raman scattering experiments are conducted on oil–paper insulation samples to accurately detect states. The categorized into different stages based the polymerization degree of insulating paper. Principal...