公开(公告)号：US20140309122A1

公开(公告)日：2014-10-16

申请号：US14243920

申请日：2014-04-03

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Yanjun Qi ,  Salim Akhter Chowdhury

IPC: G06F19/24 ,  G06N99/00

CPC classification number: G16B40/00 ,  G06N20/00 ,  G16B5/00 ,  G16B50/00

Abstract: Systems and methods are disclosed for Knowledge-Driven Sparse Learning to Identify Interpretable High-Order Feature Interactions. This is done by generating one or more functional groups from gene features and gene and protein interaction grouping; selecting informative genes and functional interactions that exhibit differential patterns for the target disease and to generate a reduced feature space; and searching exhaustively on the reduced feature space by examining all possible pairs of interacting features (and possibly higher-order feature interactions) to identify combination of markers and complex patterns of feature interactions that are informative about the phenotypes in a sparse learning framework to select informative interactions and genes.

Abstract translation: 公开了知识驱动的稀疏学习识别可解释的高阶特征交互的系统和方法。 这是通过从基因特征和基因和蛋白质相互作用分组产生一个或多个官能团来实现的; 选择信息性基因和功能相互作用，显示目标疾病的差异模式并产生减少的特征空间; 并通过检查所有可能的交互特征对（以及可能的高阶特征相互作用）来查找减少的特征空间，从而识别标记的组合和特征相互作用的复杂模式，以便在稀疏学习框架中提供关于表型的信息，以选择信息 相互作用和基因。

公开(公告)号：US09183503B2

公开(公告)日：2015-11-10

申请号：US13908715

申请日：2013-06-03

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Yanjun Qi

IPC: G06N5/02

CPC classification number: G06N5/025

Abstract: Systems and methods are provided for identifying combinatorial feature interactions, including capturing statistical dependencies between categorical variables, with the statistical dependencies being stored in a computer readable storage medium. A model is selected based on the statistical dependencies using a neighborhood estimation strategy, with the neighborhood estimation strategy including generating sets of arbitrarily high-order feature interactions using at least one rule forest and optimizing one or more likelihood functions. A damped mean-field approach is applied to the model to obtain parameters of a Markov random field (MRF); a sparse high-order semi-restricted MRF is produced by adding a hidden layer to the MRF; indirect long-range dependencies between feature groups are modeled using the sparse high-order semi-restricted MRF; and a combinatorial dependency structure between variables is output.

Abstract translation: 提供了用于识别组合特征交互的系统和方法，包括捕获分类变量之间的统计依赖性，并将统计依赖性存储在计算机可读存储介质中。 基于使用邻域估计策略的统计依赖性来选择模型，邻域估计策略包括使用至少一个规则林生成任意高阶特征交互的集合并且优化一个或多个似然函数。 将阻尼平均场方法应用于模型以获得马尔可夫随机场（MRF）的参数; 通过向MRF添加隐藏层来产生稀疏高阶半限制MRF; 特征组之间的间接长程依赖关系使用稀疏高阶半限制MRF进行建模; 并输出变量之间的组合依赖结构。

公开(公告)号：US08977579B2

公开(公告)日：2015-03-10

申请号：US13649823

申请日：2012-10-11

Applicant: NEC Laboratories America, Inc.

Inventor： Yunlong He ,  Yanjun Qi ,  Koray Kavukcuoglu

IPC: G06F15/18 ,  G06N99/00 ,  A61M5/00 ,  A61M25/10 ,  A61M25/00

CPC classification number: G06N99/005 ,  A61M5/007 ,  A61M25/0026 ,  A61M25/10 ,  A61M25/1011 ,  A61M2025/105

Abstract: Disclosed is a general learning framework for computer implementation that induces sparsity on the undirected graphical model imposed on the vector of latent factors. A latent factor model SLFA is disclosed as a matrix factorization problem with a special regularization term that encourages collaborative reconstruction. Advantageously, the model may simultaneously learn the lower-dimensional representation for data and model the pairwise relationships between latent factors explicitly. An on-line learning algorithm is disclosed to make the model amenable to large-scale learning problems. Experimental results on two synthetic data and two real-world data sets demonstrate that pairwise relationships and latent factors learned by the model provide a more structured way of exploring high-dimensional data, and the learned representations achieve the state-of-the-art classification performance.

Abstract translation: 公开了一种用于计算机实现的通用学习框架，其在对潜在因素的向量施加的无向图形模型上引起稀疏性。 潜在因素模型SLFA被公开为具有鼓励协作重建的特殊正则化术语的矩阵分解问题。 有利地，该模型可以同时学习数据的低维表示，并明确地模拟潜在因素之间的成对关系。 公开了一种在线学习算法，使得该模型适合于大规模学习问题。 两个合成数据和两个现实世界数据集的实验结果表明，该模型获得的成对关系和潜在因素提供了一种更结构化的探索高维数据的方法，并且学习的表示实现了最先进的分类 性能。

公开(公告)号：US20130325786A1

公开(公告)日：2013-12-05

申请号：US13908715

申请日：2013-06-03

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Yanjun Qi

IPC: G06N5/02

CPC classification number: G06N5/025

Abstract: Systems and methods are provided for identifying combinatorial feature interactions, including capturing statistical dependencies between categorical variables, with the statistical dependencies being stored in a computer readable storage medium. A model is selected based on the statistical dependencies using a neighborhood estimation strategy, with the neighborhood estimation strategy including generating sets of arbitrarily high-order feature interactions using at least one rule forest and optimizing one or more likelihood functions. A damped mean-field approach is applied to the model to obtain parameters of a Markov random field (MRF); a sparse high-order semi-restricted MRF is produced by adding a hidden layer to the MRF; indirect long-range dependencies between feature groups are modeled using the sparse high-order semi-restricted MRF; and a combinatorial dependency structure between variables is output.

Abstract translation: 提供了用于识别组合特征交互的系统和方法，包括捕获分类变量之间的统计依赖性，并将统计依赖性存储在计算机可读存储介质中。 基于使用邻域估计策略的统计依赖性来选择模型，邻域估计策略包括使用至少一个规则林生成任意高阶特征交互的集合并优化一个或多个似然函数。 将阻尼平均场方法应用于模型以获得马尔可夫随机场（MRF）的参数; 通过向MRF添加隐藏层来产生稀疏高阶半限制MRF; 特征组之间的间接长程依赖关系使用稀疏高阶半限制MRF进行建模; 并输出变量之间的组合依赖结构。