JMLR workshop and conference proceedings最新文献_第3页

A Statistical Model for Event Sequence Data. 事件序列数据的统计模型。

JMLR workshop and conference proceedings Pub Date : 2014-01-01

Kevin Heins, Hal Stern

引用次数: 0

Learning Heterogeneous Hidden Markov Random Fields. 学习异构隐马尔可夫随机场

JMLR workshop and conference proceedings Pub Date : 2014-01-01

Jie Liu, Chunming Zhang, Elizabeth Burnside, David Page

引用次数: 0

On A Nonlinear Generalization of Sparse Coding and Dictionary Learning. 论稀疏编码和字典学习的非线性泛化

JMLR workshop and conference proceedings Pub Date : 2013-01-01

Yuchen Xie, Jeffrey Ho, Baba Vemuri

{"title":"On A Nonlinear Generalization of Sparse Coding and Dictionary Learning.","authors":"Yuchen Xie, Jeffrey Ho, Baba Vemuri","doi":"","DOIUrl":"","url":null,"abstract":"Existing dictionary learning algorithms are based on the assumption that the data are vectors in an Euclidean vector space ℝ d , and the dictionary is learned from the training data using the vector space structure of ℝ d and its Euclidean L2-metric. However, in many applications, features and data often originated from a Riemannian manifold that does not support a global linear (vector space) structure. Furthermore, the extrinsic viewpoint of existing dictionary learning algorithms becomes inappropriate for modeling and incorporating the intrinsic geometry of the manifold that is potentially important and critical to the application. This paper proposes a novel framework for sparse coding and dictionary learning for data on a Riemannian manifold, and it shows that the existing sparse coding and dictionary learning methods can be considered as special (Euclidean) cases of the more general framework proposed here. We show that both the dictionary and sparse coding can be effectively computed for several important classes of Riemannian manifolds, and we validate the proposed method using two well-known classification problems in computer vision and medical imaging analysis.","PeriodicalId":89793,"journal":{"name":"JMLR workshop and conference proceedings","volume":"28 3","pages":"1480-1488"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3796141/pdf/nihms509436.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31811141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of Extreme Values and Associated Level Sets of a Regression Function via Selective Sampling. 通过选择性抽样估计回归函数的极值和相关水平集。

JMLR workshop and conference proceedings Pub Date : 2013-01-01

Stanislav Minsker

引用次数: 0

Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction. 混合LICORS:一种预测状态重建的非参数算法。

JMLR workshop and conference proceedings Pub Date : 2013-01-01

Georg M Goerg, Cosma Rohilla Shalizi

引用次数: 0

A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems. 非凸正则化优化问题的通用迭代收缩和阈值算法

JMLR workshop and conference proceedings Pub Date : 2013-01-01

Pinghua Gong, Changshui Zhang, Zhaosong Lu, Jianhua Z Huang, Jieping Ye

{"title":"A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems.","authors":"Pinghua Gong, Changshui Zhang, Zhaosong Lu, Jianhua Z Huang, Jieping Ye","doi":"","DOIUrl":"","url":null,"abstract":"Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterparts in several sparse learning settings. However, solving the non-convex optimization problems associated with non-convex penalties remains a big challenge. A commonly used approach is the Multi-Stage (MS) convex relaxation (or DC programming), which relaxes the original non-convex problem to a sequence of convex problems. This approach is usually not very practical for large-scale problems because its computational cost is a multiple of solving a single convex problem. In this paper, we propose a General Iterative Shrinkage and Thresholding (GIST) algorithm to solve the nonconvex optimization problem for a large class of non-convex penalties. The GIST algorithm iteratively solves a proximal operator problem, which in turn has a closed-form solution for many commonly used penalties. At each outer iteration of the algorithm, we use a line search initialized by the Barzilai-Borwein (BB) rule that allows finding an appropriate step size quickly. The paper also presents a detailed convergence analysis of the GIST algorithm. The efficiency of the proposed algorithm is demonstrated by extensive experiments on large-scale data sets.","PeriodicalId":89793,"journal":{"name":"JMLR workshop and conference proceedings","volume":"28 2","pages":"37-45"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4184274/pdf/nihms497494.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32721681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning an Internal Dynamics Model from Control Demonstration. 从控制演示中学习内部动力学模型。

JMLR workshop and conference proceedings Pub Date : 2013-01-01

Matthew D Golub, Steven M Chase, Byron M Yu

引用次数: 0

Factorized Diffusion Map Approximation. 分解扩散图近似。

JMLR workshop and conference proceedings Pub Date : 2012-01-01

Saeed Amizadeh, Hamed Valizadegan, Milos Hauskrecht

引用次数: 0

Fast, Exact Model Selection and Permutation Testing for ℓ₂-Regularized Logistic Regression. 2-正则逻辑回归的快速、精确模型选择与置换检验。

JMLR workshop and conference proceedings Pub Date : 2012-01-01

Bryan Conroy, Paul Sajda

引用次数: 0

High-Dimensional Structured Feature Screening Using Binary Markov Random Fields. 基于二元马尔可夫随机场的高维结构化特征筛选。

JMLR workshop and conference proceedings Pub Date : 2012-01-01

Jie Liu, Peggy Peissig, Chunming Zhang, Elizabeth Burnside, Catherine McCarty, David Page

{"title":"High-Dimensional Structured Feature Screening Using Binary Markov Random Fields.","authors":"Jie Liu, Peggy Peissig, Chunming Zhang, Elizabeth Burnside, Catherine McCarty, David Page","doi":"","DOIUrl":"","url":null,"abstract":"Feature screening is a useful feature selection approach for high-dimensional data when the goal is to identify all the features relevant to the response variable. However, common feature screening methods do not take into account the correlation structure of the covariate space. We propose the concept of a feature relevance network, a binary Markov random field to represent the relevance of each individual feature by potentials on the nodes, and represent the correlation structure by potentials on the edges. By performing inference on the feature relevance network, we can accordingly select relevant features. Our algorithm does not yield sparsity, which is different from the particular popular family of feature selection approaches based on penalized least squares or penalized pseudo-likelihood. We give one concrete algorithm under this framework and show its superior performance over common feature selection methods in terms of prediction error and recovery of the truly relevant features on real-world data and synthetic data.","PeriodicalId":89793,"journal":{"name":"JMLR workshop and conference proceedings","volume":"22 ","pages":"712-721"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3630518/pdf/nihms-455262.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31467899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0