Multi-View Learning over Structured and Non-Identical Outputs
نویسندگان
چکیده
In many machine learning problems, labeled training data is limited but unlabeled data is ample. Some of these problems have instances that can be factored into multiple views, each of which is nearly sufficent in determining the correct labels. In this paper we present a new algorithm for probabilistic multi-view learning which uses the idea of stochastic agreement between views as regularization. Our algorithm works on structured and unstructured problems and easily generalizes to partial agreement scenarios. For the full agreement case, our algorithm minimizes the Bhattacharyya distance between the models of each view, and performs better than CoBoosting and two-view Perceptron on several flat and structured classification problems.
منابع مشابه
Extending Long Short-Term Memory for Multi-View Structured Learning
Long Short-Term Memory (LSTM) networks have been successfully applied to a number of sequence learning problems but they lack the design flexibility to model multiple view interactions, limiting their ability to exploit multi-view relationships. In this paper, we propose a Multi-View LSTM (MV-LSTM), which explicitly models the view-specific and cross-view interactions over time or structured ou...
متن کاملPredict and Constrain: Modeling Cardinality in Deep Structured Prediction
Many machine learning problems require the prediction of multi-dimensional labels. Such structured prediction models can benefit from modeling dependencies between labels. Recently, several deep learning approaches to structured prediction have been proposed. Here we focus on capturing cardinality constraints in such models. Namely, constraining the number of non-zero labels that the model outp...
متن کاملSolving a New Multi-objective Unrelated Parallel Machines Scheduling Problem by Hybrid Teaching-learning Based Optimization
This paper considers a scheduling problem of a set of independent jobs on unrelated parallel machines (UPMs) that minimizesthe maximum completion time (i.e., makespan or ), maximum earliness ( ), and maximum tardiness ( ) simultaneously. Jobs have non-identical due dates, sequence-dependent setup times and machine-dependentprocessing times. A multi-objective mixed-integer linear programmi...
متن کاملTree-guided group lasso for multi-response regression with structured sparsity, with an application to eQTL mapping
We consider the problem of learning a sparse multi-task regression with an application to a genetic association mapping problem for discovering genetic markers that influence expression levels of multiple genes jointly. In particular, we consider the case where the structure over the outputs can be represented as a tree with leaf nodes as outputs and internal nodes as clusters of the outputs at...
متن کاملGraph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso
We consider the problem of learning a structured multi-task regression, where the output consists of multiple responses that are related by a graph and the correlated response variables are dependent on the common inputs in a sparse but synergistic manner. Previous methods such as l1/l2-regularized multi-task regression assume that all of the output variables are equally related to the inputs, ...
متن کامل