Predictive Subspace Learning for Multi-view Data: a Large Margin Approach
Learning from multi-view data is important in many applications, such as image classification and annotation. In this paper, we present a large-margin learning framework to discover a predictive latent subspace representation shared by multiple views. Our approach is based on an undirected latent space Markov network that fulfills a weak conditional independence assumption that multi-view observations and response variables are independent given a set of latent variables. We provide efficient inference and parameter estimation methods for the latent subspace model. Finally, we demonstrate the advantages of large-margin learning on real video and web image data for discovering predictive latent representations and improving the performance on image classification, annotation and retrieval.
Code (0)
등록된 구현이 없습니다.
Tasks
General Classificationimage-classificationImage Classificationparameter estimationRetrievalSimilar Papers 제목 키워드 기반
A spectral method for multi-view subspace learning using the product of projections
Multi-view data provides complementary information on the same set of observations, with multi-omics and multimodal sensor data being common examples. Analyzing such data typically requires distinguishing between shared …
DiagnosticData-driven Model Predictive Control using MATLAB
This paper presents a comprehensive overview of data-driven model predictive control, highlighting state-of-the-art methodologies and their numerical implementation. The discussion begins with a brief review of conventio…
modelModel Predictive ControlAn Information Retrieval Approach to Finding Dependent Subspaces of Multiple Views
Finding relationships between multiple views of data is essential both for exploratory analysis and as pre-processing for predictive tasks. A prominent approach is to apply variants of Canonical Correlation Analysis (CCA…
Information RetrievalRetrievalSubspace-Contrastive Multi-View Clustering
Most multi-view clustering methods are limited by shallow models without sound nonlinear information perception capability, or fail to effectively exploit complementary information hidden in different views. To tackle th…
ClusteringTheory of matching pursuit
We analyse matching pursuit for kernel principal components analysis by proving that the sparse subspace it produces is a sample compression scheme. We show that this bound is tighter than the KPCA bound of Shawe-Taylor …