In the field of fluid mechanics, dimensionality reduction (DR) is widely used for feature extraction and information simplification high-dimensional spatiotemporal data. It well known that nonlinear DR techniques outperform linear methods, this conclusion may have reached a consensus in mechanics. However, derived from an incomplete evaluation techniques. paper, we propose more comprehensive sy...

Dimension reduction techniques are widely used for the analysis and visualization of complex sets of data. This paper compares two recently published methods for nonlinear projection: Isomap and Curvilinear Distance Analysis (CDA). Contrarily to the traditional linear PCA, these methods work like multidimensional scaling, by reproducing in the projection space the pairwise distances measured in...

This paper presents an optical system for performing excitation resolved imaging of fluorescent dyes, tissue phantoms and ex vivo tissues. The excitation source was a supercontinuum generated in a highly nonlinear fibre, spectrally filtered using dispersive optics and a movable slit or digital micromirror device. This allowed excitation with multiple spectra, which may be chosen to optimize the...

In this paper, we address the problem of recognizing human motion from videos. Human motion recognition is a challenging computer vision problem. In the past ten years, a number of successful approaches based on nonlinear manifold learning have been proposed. However, little attention has been given to the use of isometric feature mapping (Isomap) for human motion recognition. Our contribution ...

Dimension reduction techniques are widely used for the analysis and visualization of complex sets of data. This paper compares two nonlinear projection methods: Isomap and Curvilinear Distance Analysis. Contrarily to the traditional linear PCA, these methods work like multidimensional scaling, by reproducing in the projection space the pairwise distances measured in the data space. They differ ...

In this paper we are investigating a developmental learning strategy for robotic applications. Two approaches based on Isomap dimensionality reduction and on feature-based learning are investigated. In the training phase, the robot is presented with objects (e.g. squares, circles) and labels regarding their properties (e.g. shape, size, orientation). From these the system learns a representatio...

We propose a novel nonlinear manifold learning from snapshot data and demonstrate its superiority over proper orthogonal decomposition (POD) for shedding-dominated shear flows. Key enablers are isometric feature mapping, Isomap, as encoder and, $K$ -nearest neighbours ( NN) algorithm decoder. The proposed technique is applied to numerical experimental datasets including the fluidic pinball, swi...

Current feature-based methods for sketch recognition systems rely on human-selected features. Certain machine learning techniques have been found to be good nonlinear features extractors. In this paper, we apply a manifold learning method, kernel Isomap, with a new algorithm for multi-stroke sketch recognition, which significantly outperforms the standard featurebased techniques. INTRODUCTION S...

This paper discusses the Isomap method for dimensionality reduction and studies its performance on both artificial and natural datasets. While linear methods for dimensionality reduction such as Principle Component Analysis (PCA) detect a linear subspace of the original domain that represents the data with maximal accuracy, the Isomap method detects the tangent space of a manifold embedded in t...