Adapting Component Analysis

Dorri, Fatemeh; Ghodsi, Ali

doi:10.1109/icdm.2012.85

Cited by 15 publications

(8 citation statements)

References 24 publications

(34 reference statements)

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“…The Hilbert Schmidt independence (HSI) that is formulated with Hilbert Schmidt norm in RKHS is used to measure the dependency between two random variables [24]. To preserve the important feature of data, Dorri et al regarded original and transformed data features as two random variants, and maximized their dependency during the probability distribution alignment [39,40]. Yan et al minimized the dependency between the projected and domain features (e.g.…”

Section: B Hilbert Schmidt Independencementioning

confidence: 99%

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A Unified Joint Maximum Mean Discrepancy for Domain Adaptation

Wang¹,

Li²,

Shu-hui³

et al. 2021

Preprint

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Domain adaptation has received a lot of attention in recent years, and many algorithms have been proposed with impressive progress. However, it is still not fully explored concerning the joint probability distribution (P (X, Y)) distance for this problem, since its empirical estimation derived from the maximum mean discrepancy (joint maximum mean discrepancy, JMMD) will involve complex tensor-product operator that is hard to manipulate. To solve this issue, this paper theoretically derives a unified form of JMMD that is easy to optimize, and proves that the marginal, class conditional and weighted class conditional probability distribution distances are our special cases with different label kernels, among which the weighted class conditional one not only can realize feature alignment across domains in the category level, but also deal with imbalance dataset using the class prior probabilities. From the revealed unified JMMD, we illustrate that JMMD degrades the feature-label dependence (discriminability) that benefits to classification, and it is sensitive to the label distribution shift when the label kernel is the weighted class conditional one. Therefore, we leverage Hilbert Schmidt independence criterion and propose a novel MMD matrix to promote the dependence, and devise a novel label kernel that is robust to label distribution shift. Finally, we conduct extensive experiments on several cross-domain datasets to demonstrate the validity and effectiveness of the revealed theoretical results.

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Section: B Hilbert Schmidt Independencementioning

confidence: 99%

“…Different from [24,[39][40][41][42], we utilize the HSI with its uncentered variant for consistency with the JMMD, and combine the JMMD and HSI in a neat format with a novel MMD matrix, i.e., (M j − δM h ). Thus, the first discovery from the unified JMMD to improve its feature discriminability is finalized as,…”

Section: A a Novel MMD Matrixmentioning

confidence: 99%

A Unified Joint Maximum Mean Discrepancy for Domain Adaptation

Wang¹,

Li²,

Shu-hui³

et al. 2021

Preprint

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“…[8] extended MMDE by Transfer Component Analysis (TCA), which learns a kernel in RKHS. [32] adopted a similar idea. [33] learns target predictive function with a low variance.…”

Section: B Transfer Learningmentioning

confidence: 99%

Stratified Transfer Learning for Cross-domain Activity Recognition

Wang

Chen

et al. 2018

2018 IEEE International Conference on Pervasive Computing and Communications (PerCom)

183

106

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In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global domain shift while ignoring the intra-affinity between classes, which will hinder the performance of the algorithms. In this paper, we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer. The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition. Specifically, STL first obtains pseudo labels for the target domain via majority voting technique. Then, it performs intra-class knowledge transfer iteratively to transform both domains into the same subspaces. Finally, the labels of target domain are obtained via the second annotation. To evaluate the performance of STL, we conduct comprehensive experiments on three large public activity recognition datasets (i.e. OPPORTU-NITY, PAMAP2, and UCI DSADS), which demonstrates that STL significantly outperforms other state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%). Furthermore, we extensively investigate the performance of STL across different degrees of similarities and activity levels between domains. And we also discuss the potential of STL in other pervasive computing applications to provide empirical experience for future research.

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“…Distribution alignment, feature selection, and subspace learning are three frequently used methods in traditional domain adaptation. There are also many methods that address the different kinds of distribution alignment, from marginal distribution alignment [32,6,27,18], to conditional distribution alignment [12,47] and finally joint alignment of these two distributions [46,48]. Feature selection methods aim to find the shared features between source and target domain [1,27].…”

Section: Related Workmentioning

confidence: 99%

Impact of ImageNet Model Selection on Domain Adaptation

Zhang

Davison

2020

Preprint

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Deep neural networks are widely used in image classification problems. However, little work addresses how features from different deep neural networks affect the domain adaptation problem. Existing methods often extract deep features from one ImageNet model, without exploring other neural networks. In this paper, we investigate how different ImageNet models affect transfer accuracy on domain adaptation problems. We extract features from sixteen distinct pre-trained ImageNet models and examine the performance of twelve benchmarking methods when using the features. Extensive experimental results show that a higher accuracy ImageNet model produces better features, and leads to higher accuracy on domain adaptation problems (with a correlation coefficient of up to 0.95). We also examine the architecture of each neural network to find the best layer for feature extraction. Together, performance from our features exceeds that of the state-of-the-art in three benchmark datasets.

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Adapting Component Analysis

Cited by 15 publications

References 24 publications

A Unified Joint Maximum Mean Discrepancy for Domain Adaptation

A Unified Joint Maximum Mean Discrepancy for Domain Adaptation

Stratified Transfer Learning for Cross-domain Activity Recognition

Impact of ImageNet Model Selection on Domain Adaptation

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