arXiv subjects

Bingqing Li

Publications and source records attributed to Bingqing Li.

6 recordsLinked to original sources

arXiv2025

Convergence and Optimality of the EM Algorithm Under Multi-Component Gaussian Mixture Models

Gaussian mixture models (GMMs) are fundamental statistical tools for modeling heterogeneous data. Due to the nonconcavity of the likelihood function, the Expectation-Maximization (EM) algorithm is widely used for parameter estimation of each Gaussian component. Existing analyses of the EM algorithm's convergence to the true parameter focus on either the two-component case or multi-component settings with known mixing probabilities and isotropic covariance matrices. In this work, we study the convergence of the EM algorithm for multi-component GMMs in full generality. The population-level EM is shown to converge to the true parameter when the smallest separation among all pairs of Gaussian components exceeds a logarithmic factor of the largest separation and the reciprocal of the minimal mixing probabilities. At the sample level, the EM algorithm is shown to be minimax rate-optimal, up to a logarithmic factor. We develop two distinct novel analytical approaches, each tailored to a different regime of separation, reflecting two complementary perspectives on the use of EM. As a byproduct of our analysis, we show that the EM algorithm, when used for community detection, also achieves the minimax optimal rate of misclustering error under milder separation conditions than spectral clustering and Lloyd's algorithm, an interesting result in its own right. Our analysis allows the number of components, the minimal mixing probabilities, the separation between Gaussian components and the dimension to grow with the sample size. Simulation studies corroborate our theoretical findings.

math.ST↗

arXiv2024

Directionally asymmetric nonlinear optics in planar chiral MnTiO$_3$

Planar chiral structures possess a two dimensional handedness that is associated with broken mirror symmetry. Such motifs span vast length scales; examples include certain pinwheel molecules, nautilus shells, cyclone wind patterns and spiral galaxies. Although pervasive in nature, it has only recently been found that condensed matter systems can exhibit a form of planar chirality through toroidal arrangements of electric dipoles, known as ferro-rotational (FR) order. A key characteristic of such order is that enantiomorph conversion occurs when the solid is flipped by 180 degrees about an in-plane axis. Consequently, ferro-rotationally ordered materials may exhibit directionally asymmetric response functions, even while preserving inversion and time-reversal symmetry. Such an effect, however, has yet to be observed. Using second harmonic interferometry, we show here that when circularly polarized light is incident on MnTiO$_3$, the generated nonlinear signal exhibits directional asymmetry. Depending on whether the incident light is parallel or anti-parallel to the FR axis, we observe a different conversion efficiency of two right (left) circularly polarized photons into a frequency-doubled left (right) circularly polarized photon. Our work uncovers a fundamentally new optical effect in ordered solids and opens up the possibility for developing novel nonlinear and directionally asymmetric optical devices.

physics.optics↗

arXiv2024

Trainable Joint Channel Estimation, Detection and Decoding for MIMO URLLC Systems

The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols. Consequently, error propagation can occur in traditional turbo receivers, leading to performance degradation. Moreover, the processing delay induced by information exchange between different modules may also be undesirable for URLLC. To address the issues, we advocate to perform joint channel estimation, detection, and decoding (JCDD) for MIMO URLLC systems encoded by short low-density parity-check (LDPC) codes. Specifically, we develop two novel JCDD problem formulations based on the maximum a posteriori (MAP) criterion for Gaussian MIMO channels and sparse mmWave MIMO channels, respectively, which integrate the pilots, the bit-to-symbol mapping, the LDPC code constraints, as well as the channel statistical information. Both the challenging large-scale non-convex problems are then solved based on the alternating direction method of multipliers (ADMM) algorithms, where closed-form solutions are achieved in each ADMM iteration. Furthermore, two JCDD neural networks, called JCDDNet-G and JCDDNet-S, are built by unfolding the derived ADMM algorithms and introducing trainable parameters. It is interesting to find via simulations that the proposed trainable JCDD receivers can outperform the turbo receivers with affordable computational complexities.

eess.SP↗

arXiv2024

A New Regression Lens on Multi-Class Classification

Linear Discriminant Analysis (LDA) is a fundamental method for classification. Its simple linear structure facilitates interpretation, and it is naturally suited to multi-class settings. LDA is also closely connected to several classical multivariate techniques, including Fisher's discriminant analysis, canonical correlation analysis, and linear regression. In this paper, we strengthen the connection between LDA and multivariate response regression by establishing an explicit relationship between discriminant directions and regression coefficients. This characterization yields a new regression-based framework for multi-class classification that accommodates structured, regularized, and even non-parametric regression methods. In contrast to existing regression-based approaches, our formulation is particularly amenable to theoretical analysis: we develop a general strategy for deriving bounds on the excess misclassification risk of the proposed classifier across all such regression procedures. As concrete applications, we provide complete theoretical guarantees for two widely used methods -- $\ell_1$-regularization and reduced-rank regression -- neither of which has previously been fully analyzed in the LDA context. The theoretical results are supported by extensive simulation studies and empirical evaluations on real data.

stat.ME↗

arXiv2020

Option Pricing Under a Discrete-Time Markov Switching Stochastic Volatility with Co-Jump Model

We consider option pricing using a discrete-time Markov switching stochastic volatility with co-jump model, which can model volatility clustering and varying mean-reversion speeds of volatility. For pricing European options, we develop a computationally efficient method for obtaining the probability distribution of average integrated variance (AIV), which is key to option pricing under stochastic-volatility-type models. Building upon the efficiency of the European option pricing approach, we are able to price an American-style option, by converting its pricing into the pricing of a portfolio of European options. Our work also provides constructive guidance for analyzing derivatives based on variance, e.g., the variance swap. Numerical results indicate our methods can be implemented very efficiently and accurately.

q-fin.PR↗

arXiv2014

Automorphisms of the cycle prefix digraph

Cycle prefix digraphs have been proposed as an efficient model of symmetric interconnection networks for parallel architecture. It has been discovered that the cycle prefix networks have many attractive communication properties. In this paper, we determine the automorphism group of the cycle prefix digraphs. We show that the automorphism group of a cycle prefix digraph is isomorphic to the symmetric group on its underlying alphabet. Our method can be applied to other classes of graphs built on alphabets including the hypercube, the Kautz graph,and the de Bruijn graph.

math.CO↗