Abstract Background Consonant clusters are a feature of many world languages. The acquisition consonant is one the latest occurring aspects speech in normally developing children. Difficulty producing has been found to contribute high levels unintelligibility children with phonological impairment. This cross-sectional descriptive study that was applied on 150 typically (TD) Egyptian Arabic chil...

Manifold learning algorithms rely on a neighbourhood graph to provide an estimate of the data’s local topology. Unfortunately, current methods for estimating local topology assume local Euclidean geometry and locally uniform data density, which often leads to poor data embeddings. We address these shortcomings by proposing a framework that combines local learning with parametric density estimat...

Heterodisulfide reductase (Hdr) from methanogenic archea is an iron-sulfur protein that catalyzes the reversible two-electron reduction of the mixed disulfide CoM-S-S-CoB to the thiol coenzymes, coenzyme M (CoM-SH) and coenzyme B (CoB-SH). It is unusual that this enzyme uses an iron-sulfur cluster to mediate disulfide reduction in two one-electron steps via site-specific cluster chemistry. Upon...

We present a fast algorithm for non-linear dimension reduction. The algorithm builds a local linear model of the data by merging PCA with clustering based on a new distortion measure. Experiments with speech and image data indicate that the local linear algorithm produces encodings with lower distortion than those built by five layer auto-associative networks. The local linear algorithm is also...

We present an algorithmic scheme for unsupervised cluster ensembles, based on randomized projections between metric spaces, by which a substantial dimensionality reduction is obtained. Multiple clusterings are performed on random subspaces, approximately preserving the distances between the projected data, and then they are combined using a pairwise similarity matrix; in this way the accuracy o...

Compressive spectral clustering combines the distance preserving measurements of compressed sensing with the power of spectral clustering. Our analysis provides rigorous bounds on how small errors in the affinity matrix can affect the spectral coordinates and clusterability. This work generalizes the current perturbation results of two-class spectral clustering to incorporate multiclass cluster...

In this paper two techniques for units clustering and factorial dimensionality reduction of variables and occasions of a three-mode data set are discussed. These techniques can be seen as the simultaneous version of two procedures based on the sequential application of k-means and Tucker2 algorithms and vice versa. The two techniques, T3Clus and 3Fk-means, have been compared theoretically and e...

In this work we will apply Diffusion Maps (DM), a recent technique for dimensionality reduction and clustering, to build local models for wind energy forecasting. We will compare ridge regression models for K–means clusters obtained over DM features, against the models obtained for clusters constructed over the original meteorological data or principal components, and also against a global mode...

As an important machine learning topic, dimensionality reduction has been widely studied and utilized in various kinds of areas. A multitude of dimensionality reduction methods have been developed, among which unsupervised dimensionality reduction is more desirable when obtaining label information requires onerous work. However, most previous unsupervised dimensionality reduction methods call f...

There have beem many studies on the analysis and design of low-noise oscillators. Recently, much attention has been paid to the noise reduction technique using coupled oscillators. When oscillators are coupled, the coupling method is very important and affects various factors, for example, the level of noise. In this study, we analyze the phase noise of multiphase CMOS LC oscillators coupled by...