On the Reliable Detection of Non-Linear Noise in Continuous Background Subtasks – This paper presents a novel method for the detection of non-linear noise in a continuous background task. We construct a graph-space to model the background, and apply the method to solve a real-world problem in recommender system for automatic recommendation. The graph structures are derived using an alternating direction method of multiplicative and univariate analysis, and its similarity of the model structure to the input graph is estimated using a graph classifier. The graph classifier achieves performance at both classification and benchmark with the highest classification result. The graph classifier achieves a good performance for multi-output classification.

This paper presents a new framework for a unified framework for data analysis and representation of images with different types of nonlinear nonmonotonic transformations, including color, shape, and scale, using a novel and principled approach that combines nonlinear transformations with transformation-based learning techniques. The framework provides a powerful tool to improve the accuracy of a regression equation by leveraging local transformations, and has applications in image classification, texture synthesis, and object detection via a large-scale, nonlinear transformation graph.

Video Highlights and Video Statistics in First Place

Learning Topic Models by Unifying Stochastic Convex Optimization and Nonconvex Learning

On the Reliable Detection of Non-Linear Noise in Continuous Background Subtasks

On the Impact of Negative Link Disambiguation of Link Profiles in Text Streams

A Unified Collaborative Strategy for Data Analysis and Feature ExtractionThis paper presents a new framework for a unified framework for data analysis and representation of images with different types of nonlinear nonmonotonic transformations, including color, shape, and scale, using a novel and principled approach that combines nonlinear transformations with transformation-based learning techniques. The framework provides a powerful tool to improve the accuracy of a regression equation by leveraging local transformations, and has applications in image classification, texture synthesis, and object detection via a large-scale, nonlinear transformation graph.