Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization

Bayesian matrix completion has been studied based on a low-rank matrix factorization formulation with promising results. However, little work has been done on Bayesian matrix completion based on the more direct spectral regularization formulation. We fill this gap by presenting a novel Bayesian matrix completion method based on spectral regularization. In order to circumvent the difficulties of dealing with the orthonormality constraints of singular vectors, we derive a new equivalent form with relaxed constraints, which then leads us to design an adaptive version of spectral regularization feasible for Bayesian inference. Our Bayesian method requires no parameter tuning and can infer the number of latent factors automatically. Experiments on synthetic and real datasets demonstrate encouraging results on rank recovery and collaborative filtering, with notably good results for very sparse matrices.Comment: Accepted to AAAI 201

Exact heat kernel on a hypersphere and its applications in kernel SVM

Many contemporary statistical learning methods assume a Euclidean feature space. This paper presents a method for defining similarity based on hyperspherical geometry and shows that it often improves the performance of support vector machine compared to other competing similarity measures. Specifically, the idea of using heat diffusion on a hypersphere to measure similarity has been previously proposed, demonstrating promising results based on a heuristic heat kernel obtained from the zeroth order parametrix expansion; however, how well this heuristic kernel agrees with the exact hyperspherical heat kernel remains unknown. This paper presents a higher order parametrix expansion of the heat kernel on a unit hypersphere and discusses several problems associated with this expansion method. We then compare the heuristic kernel with an exact form of the heat kernel expressed in terms of a uniformly and absolutely convergent series in high-dimensional angular momentum eigenmodes. Being a natural measure of similarity between sample points dwelling on a hypersphere, the exact kernel often shows superior performance in kernel SVM classifications applied to text mining, tumor somatic mutation imputation, and stock market analysis

Two sample tests for high-dimensional covariance matrices

We propose two tests for the equality of covariance matrices between two high-dimensional populations. One test is on the whole variance--covariance matrices, and the other is on off-diagonal sub-matrices, which define the covariance between two nonoverlapping segments of the high-dimensional random vectors. The tests are applicable (i) when the data dimension is much larger than the sample sizes, namely the "large $p$, small $n$" situations and (ii) without assuming parametric distributions for the two populations. These two aspects surpass the capability of the conventional likelihood ratio test. The proposed tests can be used to test on covariances associated with gene ontology terms.Comment: Published in at http://dx.doi.org/10.1214/12-AOS993 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Role of the possible Σ∗(12−)\Sigma^*(\frac{1}{2}^-) state in the Λp→Λpπ0\Lambda p \to \Lambda p \pi^0 reaction

The $\Lambda p \to \Lambda p \pi^0$ reaction near threshold is studied within an effective Lagrangian method. The production process is described by single-pion and single-kaon exchange. In addition to the role played by the $\Sigma^*(1385)$ resonance of spin-parity $J^P = 3/2^+$, the effects of a newly proposed $\Sigma^*$ ($J^P = 1/2^-$) state with mass and width around $1380$ MeV and $120$ MeV are investigated. We show that our model leads to a good description of the experimental data on the total cross section of the $\Lambda p \to \Lambda p \pi^0$ reaction by including the contributions from the possible $\Sigma^*(\frac{1}{2}^-)$ state. However, the theoretical calculations by considering only the $\Sigma^*(1385)$ resonance fail to reproduce the experimental data, especially for the enhancement close to the reaction threshold. On the other hand, it is found that the single-pion exchange is dominant. Furthermore, we also demonstrate that the angular distributions provide direct information of this reaction, hence could be useful for the investigation of the existence of the $\Sigma^*(\frac{1}{2}^-)$ state and may be tested by future experiments.Comment: 8 pages, 5 figure