Isometric Sliced Inverse Regression for Nonlinear Manifolds Learning

[[abstract]]Sliced inverse regression (SIR) was developed to find effective linear dimension-reduction directions for exploring the intrinsic structure of the high-dimensional data. In this study, we present isometric SIR for nonlinear dimension reduction, which is a hybrid of the SIR method using the geodesic distance approximation. First, the proposed method computes the isometric distance between data points; the resulting distance matrix is then sliced according to K-means clustering results, and the classical SIR algorithm is applied. We show that the isometric SIR (ISOSIR) can reveal the geometric structure of a nonlinear manifold dataset (e.g., the Swiss roll). We report and discuss this novel method in comparison to several existing dimension-reduction techniques for data visualization and classification problems. The results show that ISOSIR is a promising nonlinear feature extractor for classification applications.[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Locally Linear Landmarks for Large-Scale Manifold Learning

Spectral methods for manifold learning and clustering typically construct a graph weighted with affinities (e.g. Gaussian or shortest-path distances) from a dataset and compute eigenvectors of a graph Laplacian. With large datasets, the eigendecomposition is too expensive, and is usually approximated by solving for a smaller graph defined on a subset of the points (landmarks) and then applying the Nyström formula to estimate the eigenvectors over all points. This has the problem that the affinities between landmarks do not benefit from the remaining points and may poorly represent the data if using few landmarks. We introduce a modified spectral problem that uses all data points by constraining the latent projection of each point to be a local linear function of the landmarks ’ latent projections. This constructs a new affinity matrix between landmarks that preserves manifold structure even with few landmarks and allows one to reduce the eigenproblem size and works specially well when the desired number of eigenvectors is not trivially small. The solution also provides a nonlinear out-of-sample projection mapping that is faster and more accurate than the Nyström formula. 1

Direct observation of the dead-cone effect in quantum chromodynamics

At particle collider experiments, elementary particle interactions with large momentum transfer produce quarks and gluons (known as partons) whose evolution is governed by the strong force, as described by the theory of quantum chromodynamics (QCD) [1]. The vacuum is not transparent to the partons and induces gluon radiation and quark pair production in a process that can be described as a parton shower [2]. Studying the pattern of the parton shower is one of the key experimental tools in understanding the properties of QCD. This pattern is expected to depend on the mass of the initiating parton, through a phenomenon known as the dead-cone effect, which predicts a suppression of the gluon spectrum emitted by a heavy quark of mass m and energy E, within a cone of angular size m/E around the emitter [3]. A direct observation of the dead-cone effect in QCD has not been possible until now, due to the challenge of reconstructing the cascading quarks and gluons from the experimentally accessible bound hadronic states. Here we show the first direct observation of the QCD dead-cone by using new iterative declustering techniques [4, 5] to reconstruct the parton shower of charm quarks. This result confirms a fundamental feature of QCD, which is derived more generally from its origin as a gauge quantum field theory. Furthermore, the measurement of a dead-cone angle constitutes the first direct experimental observation of the non-zero mass of the charm quark, which is a fundamental constant in the standard model of particle physics.The direct measurement of the QCD dead cone in charm quark fragmentation is reported, using iterative declustering of jets tagged with a fully reconstructed charmed hadron.In particle collider experiments, elementary particle interactions with large momentum transfer produce quarks and gluons (known as partons) whose evolution is governed by the strong force, as described by the theory of quantum chromodynamics (QCD). These partons subsequently emit further partons in a process that can be described as a parton shower which culminates in the formation of detectable hadrons. Studying the pattern of the parton shower is one of the key experimental tools for testing QCD. This pattern is expected to depend on the mass of the initiating parton, through a phenomenon known as the dead-cone effect, which predicts a suppression of the gluon spectrum emitted by a heavy quark of mass $m_{\rm{Q}}$ and energy $E$, within a cone of angular size $m_{\rm{Q}}$/$E$ around the emitter. Previously, a direct observation of the dead-cone effect in QCD had not been possible, owing to the challenge of reconstructing the cascading quarks and gluons from the experimentally accessible hadrons. We report the direct observation of the QCD dead cone by using new iterative declustering techniques to reconstruct the parton shower of charm quarks. This result confirms a fundamental feature of QCD. Furthermore, the measurement of a dead-cone angle constitutes a direct experimental observation of the non-zero mass of the charm quark, which is a fundamental constant in the standard model of particle physics