PaperlayerView exact revision on arXiv

Source-linked AI summary

Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation

Yuan Shi, Fei Sha

arXiv:1206.6438v1cs.LGstat.ML

TL;DR

Unsupervised domain adaptation seeks to adapt classifiers from labeled source data to unlabeled target data, where target labels cannot guide optimization. The paper jointly learns a domain-invariant and target-discriminative feature space using information-theoretic objectives, and reports significant improvements on object-recognition and sentiment-analysis benchmarks. Its conclusions are bounded by the assumptions underlying discriminative clustering and by an identified open area concerning comparisons between two methods.

  • Problem

    Unsupervised domain adaptation must construct classifiers for unlabeled target data despite distribution mismatch and cannot generally use target accuracy for model selection.

  • Method

    The paper jointly learns a domain-invariant, target-discriminative feature space and optimizes information-theoretic metrics related to expected target misclassification error.

  • Results

    The proposed approach significantly outperforms other state-of-the-art methods on benchmark tasks in visual object recognition and product-review sentiment analysis.

  • Takeaways & Limitations

    The empirical results support discriminative clustering as a mechanism for domain adaptation and support jointly considering feature learning and target discrimination.

  • Takeaways & Limitations

    The paper identifies a comparison between two methods as future research and assumes discriminative clustering with aligned, class-corresponding source and target clusters.

Abstract

from arXiv · show

We study the problem of unsupervised domain adaptation, which aims to adapt classifiers trained on a labeled source domain to an unlabeled target domain. Many existing approaches first learn domain-invariant features and then construct classifiers with them. We propose a novel approach that jointly learn the both. Specifically, while the method identifies a feature space where data in the source and the target domains are similarly distributed, it also learns the feature space discriminatively, optimizing an information-theoretic metric as an proxy to the expected misclassification error on the target domain. We show how this optimization can be effectively carried out with simple gradient-based methods and how hyperparameters can be cross-validated without demanding any labeled data from the target domain. Empirical studies on benchmark tasks of object recognition and sentiment analysis validated our modeling assumptions and demonstrated significant improvement of our method over competing ones in classification accuracies.

1. Introduction

Unsupervised domain adaptation must derive classifiers for unlabeled target data despite distribution mismatch and no target labels for optimization. The paper addresses this by jointly learning domain-invariant, target-discriminative features, and reports significant gains on visual recognition and sentiment analysis.

  • Motivation: Unsupervised domain adaptation transfers classifiers from labeled source data to unlabeled target data whose distribution may differ from training data.The target domain provides no labels for directly optimizing classifiers or validating target classification accuracy.
  • Existing approaches: Existing methods commonly use two stages: learn or augment domain-invariant features, then train a classifier with source labels.Examples include covariate-shift weighting, structural correspondence learning, and nonlinear feature transformations.
  • Limitation of prior practice: Maximizing similarity between marginal feature distributions alone need not preserve posterior discrimination, so an arbitrary aligned space may classify the target poorly.Projecting onto irrelevant feature dimensions can make source and target domains appear similar without retaining useful class information.
  • Proposed approach: The paper proposes a one-stage algorithm that jointly learns a domain-invariant feature space and optimizes information-theoretic metrics related to target-domain discrimination.Its central challenge is retaining discriminative information while learning without target labels.
  • Empirical findings: Significant improvements over competing state-of-the-art methods were observed on benchmark tasks in visual object recognition and product-review sentiment analysis.The empirical results also support the paper’s modeling assumptions and hypothesis about discriminative clustering.

2. Discriminative Clustering for Domain Adaptation

The method assumes that source and target data form discriminative, class-corresponding clusters whose same-class clusters are geometrically aligned. These assumptions support optimizing a domain-invariant feature space through a proxy for target misclassification error despite missing target labels.

  • Discriminative clustering: Discriminative clustering assumes that source and target data are clustered in a feature space where cluster identities correspond to class labels.The assumptions include discriminative separation of the data into class-related clusters.
  • Cluster alignment: Clusters representing the same class across source and target domains are assumed to be geometrically close.Figure 1 illustrates this cross-domain alignment together with class-related clustering.
  • Scope of assumptions: These assumptions do not require identical marginal or posterior distributions across domains and are presented as applicable to multimodal data.The paper notes that mixture-model data can exhibit modes corresponding to class labels.
  • Optimization: The alignment assumption provides a proxy for target misclassification error that can be optimized without target labels.The objective is to identify a domain-invariant feature space while approximately minimizing expected target error.

3. Proposed Approach

The approach jointly learns a latent feature space that supports discriminative clustering while aligning source and target domains, using information-theoretic objectives without target labels. It combines source classification accuracy, target-label mutual information, and domain discriminability to learn a linear transformation.

  • Core assumptions: The method assumes a latent space where source and target data form label-corresponding, well-separated clusters that are geometrically close across domains.These assumptions support transferring source labels to target instances through neighborhood-based posteriors.
  • Feature space and conditional model: A linear transformation L maps the original features into a latent space, inducing a low-rank Mahalanobis distance used for 1-NN classification.The method fixes k = 1 to avoid cross-validating the neighborhood parameter.
  • Feature space and conditional model: The conditional nearest-neighbor model estimates class posteriors from distances to labeled points and their class indicators.The resulting posterior assigns probability to each class according to the normalized probabilities of neighboring labeled instances.
  • Source discriminative clustering: The source objective minimizes expected 1-NN classification error estimated by leave-one-out accuracy on labeled source data.Optimizing this term alone would recover a metric-learning approach, without using target-domain information.
  • Target discriminative clustering: For unlabeled target data, the method maximizes mutual information between instances and estimated labels rather than directly measuring classification error.Entropy reduction encourages concentrated posteriors, while mutual information avoids assigning every target point to one class.
  • Target discriminative clustering: Using the empirical target-label prior can still yield degenerate solutions when source labels are uniformly distributed.The prior is estimated from the average target posterior rather than directly from source-label frequencies.
  • Source–target discriminability: The method minimizes mutual information between instances and binary domain labels so source and target neighborhoods become difficult to distinguish, while allowing some outliers.This avoids forcing the domains to be exactly identical, which could produce a degenerate mapping such as sending every point to the origin.

4. Experimental Results

Experiments evaluate the proposed method on object recognition and sentiment analysis under unsupervised domain adaptation. It performs strongly across benchmark domain pairs, while results support jointly learning discriminative and domain-invariant representations.

  • Experimental setup: The evaluation covers object recognition and sentiment analysis of product reviews, using multiple domains and repeated random trials.Object recognition uses four image databases and 20 trials per source-target pair; sentiment analysis uses four product domains and 10 trials per pair.
  • Experimental setup: The method learns a feature transformation and applies 1-nearest neighbor to classify transformed target instances.The transformation is learned by solving equation (7), and 1-NN avoids tuning the number of neighbors.
  • Comparison protocol: The study reports classification accuracies and standard errors for the compared methods, with best-performing algorithms identified by statistical significance within one standard error.The experiments include baselines and recently proposed adaptation methods; some baseline results use target labels for tuning.
  • Object recognition: On object recognition, the method performs best on 5 of 6 evaluated domain pairs and has a large margin over competing methods.On DSLR-Amazon, LMNN performs better, but the proposed method remains significantly better than the other methods.
  • Object recognition: LMNN outperforming domain-adaptation methods supports the authors’ concern that maximizing marginal similarity alone may not preserve discriminative information.The authors specifically connect this result to the limitations of two-stage schemes such as TCA and GFS.
  • Interpretation: Results support one-stage learning that jointly identifies discriminative clustering and low-dimensional feature spaces as important for domain adaptation.The paper interprets the object-recognition results as supporting its central modeling argument.
  • Sentiment analysis: On sentiment analysis, the proposed method performs better on 2 of 4 domain pairs but is slightly worse than SCL on the other two.Both SCL and the proposed method significantly outperform the other methods.
  • Limitations: The relative strengths and weaknesses of the proposed method and SCL remain an open subject for future research.This limitation is stated specifically for the sentiment-analysis comparison.

5. Related Work

Related work includes information-theoretic methods for semi-supervised learning, model selection in domain adaptation, and discriminative clustering. The paper distinguishes its approach by jointly learning the feature space and discriminative structure for adaptation.

  • Information-theoretic learning: Information-theoretic semi-supervised learning reduces label confusability on unlabeled data but assumes a shared data distribution.Because the distributions are assumed identical, that line of work does not learn a domain-invariant feature space.
  • Domain-adaptation model selection: A prior domain-adaptation criterion uses conditional entropy for model selection without target labels but retains a two-stage formulation.It refines parameters on unlabeled target data from an initial model tuned on labeled source data.
  • Discriminative clustering: Regularized information maximization learns clustering assignments by maximizing mutual information between data and cluster identities.That work uses a parametric model for posterior cluster probabilities and extends to semi-supervised clustering.

6. Conclusion

The paper concludes that jointly learning a domain-invariant feature space and information-theoretic discriminative classification objective is effective for unsupervised domain adaptation. Its experiments support discriminative clustering assumptions and motivate future nonlinear extensions.

  • Conclusion: The proposed one-stage approach jointly learns a domain-invariant feature space and optimizes information-theoretic metrics related to discriminative target classification.This contrasts with approaches that decouple feature learning from discriminative classifier learning.
  • Conclusion: Empirical results support the assumption that source and target data are discriminatively clustered.The authors report that exploiting clustering structures can improve existing decoupled approaches.
  • Future work: The authors plan to study discriminative learning of nonlinear feature transformations for domain adaptation.The proposed work described here uses a feature transformation, while nonlinear transformation learning is identified as future work.

Acknowlegements

The work acknowledges partial support from DARPA, the NSF, and a USC Annenberg Fellowship.

  • Acknowledgements: The research was partially supported by DARPA, NSF, and a USC Annenberg Fellowship.The cited support includes DARPA grant D11AP00278, NSF grant IIS-1065243, and a fellowship to Y. Shi.
Loading 1206.6438v1…