This article concerns the tests for the equality of two location parameters when the data dimension is larger than the sample size. Existing spatial-sign-based procedures are not robust with respect to high dimensionality, producing tests with the type-I error rates that aremuch larger than thenominal levels.Wedevelop a correction thatmakes the sign-based tests applicable for high-dimensional d...

A three layer perceptron network is used to classify the /i/ sound using isolated words from diierent speakers. A classiication accuracy of 97% has been achieved. A map of phonemes is used to trace trajectories of utterances using the self-organising neural network. A crinkle factor is proposed which allows using the self-organising map to determine the inherent dimensionality of a set of point...

We model dependencies between m multivariate continuous-valued information sources by a combination of (i) a generalized canonical correlations analysis (gCCA) to reduce dimensionality while preserving dependencies in m - 1 of them, and (ii) summarizing dependencies with the remaining one by associative clustering. This new combination of methods avoids multiway associative clustering which wou...

A lower-bound on `2 dimensionality reduction The main focus of this lecture is a lower bound on the dimension when doing dimensionality reduction with -distortion in `1 and `2. In particular, it will be shown that there exist graphs requiring Ω(log n) dimensions to embed with any fixed desired distortion in Euclidean space. The main technical result is: Theorem 1 (Alon [1]). Let v1, . . . , vn+...

The purpose of this study was to develop a reliable and valid instrument measure primary school students’ sexual reproductive health (SRH) knowledge. measures knowledge in four domains: i) puberty; ii) human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS) sexually transmitted diseases; iii) prevention child abuse; iv) reproduction systems. All items the were reviewed scree...

We model dependencies between m multivariate continuous-valued information sources by a combination of (i) a generalized canonical correlations analysis (gCCA) to reduce dimensionality while preserving dependencies in m − 1 of them, and (ii) summarizing dependencies with the remaining one by associative clustering. This new combination of methods avoids multiway associative clustering which wou...

Representations. For the purposes of this paper a representation of G over C is a matrix-valued function ρ : G → Cdρ×dρ such that ρ(x)ρ(y) = ρ(xy) for any x, y ∈ G. We call dρ the order or the dimensionality of ρ. Note that ρ(e) = I for any representation. Two representations ρ1 and ρ2 of the same dimensionality d are said to be equivalent if for some invertible T ∈ Cd×d, ρ1(x) = Tρ2(x)T for an...

A modification of the transport intensity equation with fractal organization models to account for intracellular refractive index turbulence is presented. The study demonstrates how propagations through fluctuations can reveal nonintuitive interconnections between dimensionality and dispersion in picometer micrometer wavelength range, providing insights into bridging resolution gap optical elec...

Nonnegative matrix factorization (NMF) is a linear dimensionality technique for nonnegative data with applications such as image analysis, text mining, audio source separation, and hyperspectral unmixing. Given MM rank rr, NMF looks WW rr columns HH rows that M ≈ WHM≈WH. NP-hard to solve in general. However, it can be computed efficiently under the separability assumption which requires basis v...