A Convex Formulation for Spectral Shrunk Clustering

Spectral clustering is a fundamental technique in the field of data mining and information processing. Most existing spectral clustering algorithms integrate dimensionality reduction into the clustering process assisted by manifold learning in the original space. However, the manifold in reduced-dimensional subspace is likely to exhibit altered properties in contrast with the original space. Thus, applying manifold information obtained from the original space to the clustering process in a low-dimensional subspace is prone to inferior performance. Aiming to address this issue, we propose a novel convex algorithm that mines the manifold structure in the low-dimensional subspace. In addition, our unified learning process makes the manifold learning particularly tailored for the clustering. Compared with other related methods, the proposed algorithm results in more structured clustering result. To validate the efficacy of the proposed algorithm, we perform extensive experiments on several benchmark datasets in comparison with some state-of-the-art clustering approaches. The experimental results demonstrate that the proposed algorithm has quite promising clustering performance.Comment: AAAI201

Constraining Primordial Magnetic Fields with Future Cosmic Shear Surveys

The origin of astrophysical magnetic fields observed in galaxies and clusters of galaxies is still unclear. One possibility is that primordial magnetic fields generated in the early Universe provide seeds that grow through compression and turbulence during structure formation. A cosmological magnetic field present prior to recombination would produce substantial matter clustering at intermediate/small scales, on top of the standard inflationary power spectrum. In this work we study the effect of this alteration on one particular cosmological observable, cosmic shear. We adopt the semi-analytic halo model in order to describe the non-linear clustering of matter, and feed it with the altered mass variance induced by primordial magnetic fields. We find that the convergence power spectrum is, as expected, substantially enhanced at intermediate/small angular scales, with the exact amplitude of the enhancement depending on the magnitude and power-law index of the magnetic field power spectrum. We use the predicted statistical errors for a future wide-field cosmic shear survey, on the model of the ESA Cosmic Vision mission \emph{Euclid}, in order to forecast constraints on the amplitude of primordial magnetic fields as a function of the spectral index. We find that the amplitude will be constrained at the level of $\sim 0.1$ nG for $n_B\sim -3$, and at the level of $\sim 10^{-7}$ nG for $n_B\sim 3$. The latter is at the same level of lower bounds coming from the secondary emission of gamma-ray sources, implying that for high spectral indices \emph{Euclid} will certainly be able to detect primordial magnetic fields, if they exist. The present study shows how large-scale structure surveys can be used for both understanding the origins of astrophysical magnetic fields and shedding new light on the physics of the pre-recombination Universe. (abridged)Comment: 24 pages, 9 figures. To appear on JCA

Magnification bias as a novel probe for primordial magnetic fields

In this paper we investigate magnetic fields generated in the early Universe. These fields are important candidates at explaining the origin of astrophysical magnetism observed in galaxies and galaxy clusters, whose genesis is still by and large unclear. Compared to the standard inflationary power spectrum, intermediate to small scales would experience further substantial matter clustering, were a cosmological magnetic field present prior to recombination. As a consequence, the bias and redshift distribution of galaxies would also be modified. Hitherto, primordial magnetic fields (PMFs) have been tested and constrained with a number of cosmological observables, e.g. the cosmic microwave background radiation, galaxy clustering and, more recently, weak gravitational lensing. Here, we explore the constraining potential of the density fluctuation bias induced by gravitational lensing magnification onto the galaxy-galaxy angular power spectrum. Such an effect is known as magnification bias. Compared to the usual galaxy clustering approach, magnification bias helps in lifting the pathological degeneracy present amongst power spectrum normalisation and galaxy bias. This is because magnification bias cross-correlates galaxy number density fluctuations of nearby objects with weak lensing distortions of high-redshift sources. Thus, it takes advantage of the gravitational deflection of light, which is insensitive to galaxy bias but powerful in constraining the density fluctuation amplitude. To scrutinise the potentiality of this method, we adopt a deep and wide-field spectroscopic galaxy survey. We show that magnification bias does contain important information on primordial magnetism, which will be useful in combination with galaxy clustering and shear. We find we shall be able to rule out at 95.4% CL amplitudes of PMFs larger than 0.0005 nG for values of the PMF power spectral index ~0.Comment: 21 pages, 9 figures; published on JCA

Simplified Energy Landscape for Modularity Using Total Variation

Networks capture pairwise interactions between entities and are frequently used in applications such as social networks, food networks, and protein interaction networks, to name a few. Communities, cohesive groups of nodes, often form in these applications, and identifying them gives insight into the overall organization of the network. One common quality function used to identify community structure is modularity. In Hu et al. [SIAM J. App. Math., 73(6), 2013], it was shown that modularity optimization is equivalent to minimizing a particular nonconvex total variation (TV) based functional over a discrete domain. They solve this problem, assuming the number of communities is known, using a Merriman, Bence, Osher (MBO) scheme. We show that modularity optimization is equivalent to minimizing a convex TV-based functional over a discrete domain, again, assuming the number of communities is known. Furthermore, we show that modularity has no convex relaxation satisfying certain natural conditions. We therefore, find a manageable non-convex approximation using a Ginzburg Landau functional, which provably converges to the correct energy in the limit of a certain parameter. We then derive an MBO algorithm with fewer hand-tuned parameters than in Hu et al. and which is 7 times faster at solving the associated diffusion equation due to the fact that the underlying discretization is unconditionally stable. Our numerical tests include a hyperspectral video whose associated graph has 2.9x10^7 edges, which is roughly 37 times larger than was handled in the paper of Hu et al.Comment: 25 pages, 3 figures, 3 tables, submitted to SIAM J. App. Mat

Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization

We present a graph-based variational algorithm for classification of high-dimensional data, generalizing the binary diffuse interface model to the case of multiple classes. Motivated by total variation techniques, the method involves minimizing an energy functional made up of three terms. The first two terms promote a stepwise continuous classification function with sharp transitions between classes, while preserving symmetry among the class labels. The third term is a data fidelity term, allowing us to incorporate prior information into the model in a semi-supervised framework. The performance of the algorithm on synthetic data, as well as on the COIL and MNIST benchmark datasets, is competitive with state-of-the-art graph-based multiclass segmentation methods.Comment: 16 pages, to appear in Springer's Lecture Notes in Computer Science volume "Pattern Recognition Applications and Methods 2013", part of series on Advances in Intelligent and Soft Computin