Ensemble Manifold Regularization

Authors:
Bo Geng;Dacheng Tao;Chao Xu;Linjun Yang;Xian-Sheng Hua
Affiliations:
Peking University, Beijing;The Nanyang Technological University, Singapore;Peking University, Beijing;Microsoft Research Asia, Beijing;Microsoft Research Asia, Beijing
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2012

Citing 0
Cited 18

LF-EME: Local features with elastic manifold embedding for human action recognition

Neurocomputing
A sensing and etiquette reputation-based trust management for centralized cognitive radio networks

Neurocomputing
Generalization performance of magnitude-preserving semi-supervised ranking with graph-based regularization

Information Sciences: an International Journal
Locally regularized sliced inverse regression based 3D hand gesture recognition on a dance robot

Information Sciences: an International Journal
A sparse representation method of bimodal biometrics and palmprint recognition experiments

Neurocomputing
Multiple kernel local Fisher discriminant analysis for face recognition

Signal Processing
Learning colours from textures by sparse manifold embedding

Signal Processing
From the idea of "sparse representation" to a representation-based transformation method for feature extraction

Neurocomputing
Multiple graph regularized nonnegative matrix factorization

Pattern Recognition
Cross-domain sparse coding

Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Face recognition using Weber local descriptors

Neurocomputing
Similar handwritten Chinese character recognition by kernel discriminative locality alignment

Pattern Recognition Letters
A Rayleigh-Ritz style method for large-scale discriminant analysis

Pattern Recognition
Beyond cross-domain learning: Multiple-domain nonnegative matrix factorization

Engineering Applications of Artificial Intelligence
Sparse Discriminative Information Preservation for Chinese character font categorization

Neurocomputing
Multiple spatial pooling for visual object recognition

Neurocomputing
Learning performance of coefficient-based regularized ranking

Neurocomputing
Embedded local feature selection within mixture of experts

Information Sciences: an International Journal

Quantified Score

Hi-index	0.14

Visualization

Abstract

We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning (SSL) problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, cross validation is applied, but it does not necessarily scale up. Other problems derive from the suboptimality incurred by discrete grid search and the overfitting. Therefore, we develop an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR carefully so it 1) learns both the composite manifold and the semi-supervised learner jointly, 2) is fully automatic for learning the intrinsic manifold hyperparameters implicitly, 3) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption, and 4) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Furthermore, we prove the convergence property of EMR to the deterministic matrix at rate root-n. Extensive experiments over both synthetic and real data sets demonstrate the effectiveness of the proposed framework.