Data-Mining

On the Cluster-aware Supervised Learning (CluSL): Frameworks, Convergent Algorithms, and Applications

  • Shutong Chen
  • Weijun Xie
    • Categories Data-Mining Tags , , , , ,

    This paper proposes a cluster-aware supervised learning (CluSL) framework, which integrates the clustering analysis with supervised learning (SL). The objective of CluSL is to simultaneously find the best clusters of the data points and minimize the sum of loss functions within each cluster. This framework has many potential applications in healthcare, operations management, manufacturing, and … Read more

    A sparse semismooth Newton based augmented Lagrangian method for large-scale support vector machines

  • Dunbiao Niu
  • Enbin Song
  • Peipei Tang
  • Qingsong Wang
  • Chengjing Wang
    • Categories Convex and Nonsmooth Optimization, Data-Mining Tags , ,

    Support vector machines (SVMs) are successful modeling and prediction tools with a variety of applications. Previous work has demonstrated the superiority of the SVMs in dealing with the high dimensional, low sample size problems. However, the numerical difficulties of the SVMs will become severe with the increase of the sample size. Although there exist many … Read more

    Distance geometry and data science

  • Leo Liberti
    • Categories Data-Mining, Global Optimization Applications, Graphs and Matroids Tags , , , , ,

    Data are often represented as graphs. Many common tasks in data science are based on distances between entities. While some data science methodologies natively take graphs as their input, there are many more that take their input in vectorial form. In this survey we discuss the fundamental problem of mapping graphs to vectors, and its … Read more

    Optimal K-Thresholding Algorithms for Sparse Optimization Problems

  • Yun-Bin Zhao
    • Categories Constrained Nonlinear Optimization, Convex Optimization, Data-Mining Tags , , , , ,

    The simulations indicate that the existing hard thresholding technique independent of the residual function may cause a dramatic increase or numerical oscillation of the residual. This inherit drawback of the hard thresholding renders the traditional thresholding algorithms unstable and thus generally inefficient for solving practical sparse optimization problems. How to overcome this weakness and develop … Read more

    An analysis of noise folding for low-rank matrix recovery

  • Wang Hailin
  • Jianjun Wang
  • Jianwen Huang
  • Wang Wendong
  • Feng Zhang
    • Categories Approximation Algorithms, Convex Optimization, Data-Mining

    Previous work regarding low-rank matrix recovery has concentrated on the scenarios in which the matrix is noise-free and the measurements are corrupted by noise. However, in practical application, the matrix itself is usually perturbed by random noise preceding to measurement. This paper concisely investigates this scenario and evidences that, for most measurement schemes utilized in … Read more

    High-Order Evaluation Complexity for Convexly-Constrained Optimization with Non-Lipschitzian Group Sparsity Terms

  • Xiaojun Chen
  • Philippe L. Toint
    • Categories Constrained Nonlinear Optimization, Data-Mining, Statistics Tags , , , , ,

    This paper studies high-order evaluation complexity for partially separable convexly-constrained optimization involving non-Lipschitzian group sparsity terms in a nonconvex objective function. We propose a partially separable adaptive regularization algorithm using a $p$-th order Taylor model and show that the algorithm can produce an (epsilon,delta)-approximate q-th-order stationary point in at most O(epsilon^{-(p+1)/(p-q+1)}) evaluations of the objective … Read more

    Approximating L1-Norm Best-Fit Lines

  • J. Paul Brooks
  • JH Dulá
    • Categories Data-Mining

    Sufficient conditions are provided for a deterministic algorithm for estimating an L1-norm best-fit one-dimensional subspace. To prove the conditions are sufficient, fundamental properties of the L1-norm projection of a point onto a one-dimensional subspace are derived. Also, an equivalence is established between the algorithm, which involves the calculation of several weighted medians, and independently-derived algorithms … Read more

    Group sparse recovery in impulsive noise via alternating direction method of multipliers

  • Jianjun Wang
  • Jianwen Huang
  • Wang Wendong
  • Feng Zhang
    • Categories Applications - Science and Engineering, Control Applications, Data-Mining

    In this paper, we consider the recovery of group sparse signals corrupted by impulsive noise. In some recent literature, researchers have utilized stable data fitting models, like $l_1$-norm, Huber penalty function and Lorentzian-norm, to substitute the $l_2$-norm data fidelity model to obtain more robust performance. In this paper, a stable model is developed, which exploits … Read more

    Feature selection in SVM via polyhedral k-norm

  • Manlio Gaudioso
  • Enrico Gorgone
  • Jean-Baptiste Hiriart-Urruty
    • Categories Data-Mining, Global Optimization Tags , , , ,

    We treat the Feature Selection problem in the Support Vector Machine (SVM) framework by adopting an optimization model based on use of the $\ell_0$ pseudo–norm. The objective is to control the number of non-zero components of normal vector to the separating hyperplane, while maintaining satisfactory classification accuracy. In our model the polyhedral norm $\|.\|_{[k]}$, intermediate … Read more

    Adaptive regularization algorithms with inexact evaluations for nonconvex optimization

  • Stefania Bellavia
  • Benedetta Morini
  • Philippe L. Toint
  • Gianmarco Gurioli
    • Categories Constrained Nonlinear Optimization, Data-Mining, Unconstrained Optimization Tags , , , ,

    A regularization algorithm using inexact function values and inexact derivatives is proposed and its evaluation complexity analyzed. This algorithm is applicable to unconstrained problems and to problems with inexpensive constraints (that is constraints whose evaluation and enforcement has negligible cost) under the assumption that the derivative of highest degree is beta-H\”{o}lder continuous. It features a … Read more