Recognizing a face with significant lighting, disguise and occlusion variations is an interesting and challenging problem in pattern recognition. To address this problem, many regression based methods, represented by sparse representation classifier (SRC), are presented recently. SRC uses the L1-norm to characterize the pixel-level sparse noise but ignore the spatial information of noise. In th...

Recommender systems are popular information filtering systems used in various domains. Cold-start problem is a key challenge in a recommender system. In newitem/existing-user case of the cold-start problem, which is recommendation of a recentlyarrived item to a user with historical data, finding links between existing items with recently-arrived items is critical. Using VideoLectures.net Cold-S...

We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLM), a new class of methods for nonparametric regression. Given a data set of input-response pairs, the DP-GLM produces a global model of the joint distribution through a mixture of local generalized linear models. DP-GLMs allow both continuous and categorical inputs, and can model the same class of responses that can be mo...

In this paper, we propose a computationally efficient approach -space(Sparse PArtial Correlation Estimation)- for selecting non-zero partial correlations under the high-dimension-low-sample-size setting. This method assumes the overall sparsity of the partial correlation matrix and employs sparse regression techniques for model fitting. We illustrate the performance of space by extensive simula...

In many segmentation scenarios, labeled images contain rich structural information about spatial arrangement and shapes of the objects. Integrating this rich information into supervised learning techniques is promising as it generates models which go beyond learning class association, only. This paper proposes a new supervised forest model for joint classification-regression which exploits both...

We propose new families of models and algorithms for high-dimensional nonparametric learning with joint sparsity constraints. Our approach is based on a regularization method that enforces common sparsity patterns across different function components in a nonparametric additive model. The algorithms employ a coordinate descent approach that is based on a functional soft-thresholding operator. T...

BACKGROUND The American Joint Committee on Cancer and the College of American Pathologists provide guidelines for reporting pathologic response to neoadjuvant treatment of rectal cancer. The clinical relevance of these tumor regression grading guidelines is undefined. OBJECTIVE This study evaluates the prognostic significance of the American Joint Committee on Cancer/College of American Patho...

We consider the standard non-parametric regression model with Gaussian errors but where the data consist of different samples. The question to be answered is whether the samples can be adequately represented by the same regression function. To do this we define for each sample a universal, honest and non-asymptotic confidence region for the regression function. Any subset of the samples can be ...

OBJECTIVE The objective of this study was to determine predictors of 1-year remission in early RA (ERA) using baseline and 3-month data. METHODS The Canadian Early Arthritis Cohort (CATCH) patients were included if baseline, 3- and 12-month data were available. Regression analyses for four different definitions of remission at 12 months were done to determine baseline and 3-month predictors o...

[Purpose] This study examined the relationships between joint moment and the control of the vertical ground reaction force during walking in the elderly and young male and female individuals. [Subjects and Methods] Forty elderly people, 65 years old or older (20 males and 20 females), and 40 young people, 20 to 29 years old (20 males and 20 females), participated in this study. Joint moment and...