The multi-kernel maximum mean discrepancies (MMD) between the two domains in multiple layers are minimized to adapt the learned representations from supervised learning in the source domain to be applied in the target domain. The domain-invariant features can be efficiently extracted in this way, ...
NIPS-08 Learning Bounds for Domain Adaptation COLT-09 Domain adaptation: Learning bounds and algorithms MMD paper:A Hilbert Space Embedding for Distributions and A Kernel Two-Sample Test Multi-kernel MMD paper: Optimal kernel choice for large-scale two-sample tests4...
NIPS-08 Learning Bounds for Domain Adaptation COLT-09 Domain adaptation: Learning bounds and algorithms MMD paper:A Hilbert Space Embedding for Distributions and A Kernel Two-Sample Test Multi-kernel MMD paper: Optimal kernel choice for large-scale two-sample tests4...
To this end, a Kernel Adaptive Filter (KAF) algorithm extracts the dynamic of each channel, relying on the similarity between multiple realizations through the Maximum Mean Discrepancy (MMD) criterion. To assemble dynamics extracted from all MoCap data, center kernel alignment (CKA) is used to ...
To run SampleQC, the user first runs the function calculate_pairwise_mmds, which estimates MMD values for each pair of samples, identifies groups of samples with similar QC metric distributions, and calculates embeddings in lower-dimensional spaces (both MDS and UMAP). The user can then plot ...
Inspired by the two-sample test [17], domain discrepancy based methods, e.g., shallow-model-based TCA [37], JDA [1]; deep-model-based DAN [29], WMMD [48], RTN [30], leverage different distribution measures as domain regular- izer to attain domain-invariant feature. Adversarial ...
In the domain adaptation module, the marginal and conditional distributions are adjusted using multi-kernel maximum mean discrepancy (MK-MMD) and multiple domain discriminators in the source and target domains, and an adaptive factor is designed to dynamically measure the relative importance of these ...
Therefore, it verifies that the MKJDA can construct a more effective and robust representation for cross-domain classification tasks of the bearing fault diagnosis because the dynamic distribution alignment and multi-kernel MMD are introduced into the TL method. Similarly, the statistical filter also ...
We present an adaptive multilevel mahalanobis-based dimensionality reduction (MMDR) technique for high-dimensional indexing. Our MMDR technique has three ... Jin,H.,Ooi,... - International Conference on Data Engineering 被引量: 0发表: 2003年 Recursive Support Vector Machines for Dimensionality Red...
knownastargetdomain.Thatresultsinseriousdiagnosis performancedegradation.Thispaperproposesanoveldomainadaptationmethod forrollingbearingfaultdiagnosisbasedondeeplearningtechniques.Adeepconvo- lutionalneuralnetworkisusedasthemainarchitecture.Themulti-kernelmaximum meandiscrepancies(MMD)betweenthetwodomainsinmultiplelayersare...