On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
Kernel independent component analysis
The Journal of Machine Learning Research
Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces
The Journal of Machine Learning Research
ICA using spacings estimates of entropy
The Journal of Machine Learning Research
Statistical Consistency of Kernel Canonical Correlation Analysis
The Journal of Machine Learning Research
A dependence maximization view of clustering
Proceedings of the 24th international conference on Machine learning
Supervised feature selection via dependence estimation
Proceedings of the 24th international conference on Machine learning
A kernel-based causal learning algorithm
Proceedings of the 24th international conference on Machine learning
Learning subspace kernels for classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Hilbert Space Embedding for Distributions
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Distribution-Free Learning of Bayesian Network Structure
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Nonparametric Independence Tests: Space Partitioning and Kernel Approaches
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Microarray Design Using the Hilbert---Schmidt Independence Criterion
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Detecting the direction of causal time series
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Semi-supervised Discriminant Analysis Based on Dependence Estimation
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Multi-label dimensionality reduction via dependence maximization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Fast kernel-based independent component analysis
IEEE Transactions on Signal Processing
Learning an Efficient Texture Model by Supervised Nonlinear Dimensionality Reduction Methods
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Twin Gaussian Processes for Structured Prediction
International Journal of Computer Vision
Supervised feature extraction using Hilbert-Schmidt norms
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Identifying confounders using additive noise models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Generalized clustering via kernel embeddings
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Non-parametric kernel ranking approach for social image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Multilabel dimensionality reduction via dependence maximization
ACM Transactions on Knowledge Discovery from Data (TKDD)
Consistent Nonparametric Tests of Independence
The Journal of Machine Learning Research
Feature Selection by Approximating the Markov Blanket in a Kernel-Induced Space
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Incorporating the loss function into discriminative clustering of structured outputs
IEEE Transactions on Neural Networks
A General Framework for Analyzing Data from Two Short Time-Series Microarray Experiments
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Guided Locally Linear Embedding
Pattern Recognition Letters
From bilingual dictionaries to interlingual document representations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Dual active feature and sample selection for graph classification
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model
The Journal of Machine Learning Research
A Family of Simple Non-Parametric Kernel Learning Algorithms
The Journal of Machine Learning Research
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Newton-like methods for nonparametric independent component analysis
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
The Journal of Machine Learning Research
Algorithms for learning kernels based on centered alignment
The Journal of Machine Learning Research
Feature selection via dependence maximization
The Journal of Machine Learning Research
Linear semi-supervised projection clustering by transferred centroid regularization
Journal of Intelligent Information Systems
Learning with weak views based on dependence maximization dimensionality reduction
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Transfer joint embedding for cross-domain named entity recognition
ACM Transactions on Information Systems (TOIS)
Clustering-based anomaly detection in multi-view data
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Discriminative functional analysis of human movements
Pattern Recognition Letters
Measuring statistical dependence via the mutual information dimension
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Change-point detection with feature selection in high-dimensional time-series data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Supervised Distance Preserving Projections
Neural Processing Letters
Generating multiple alternative clusterings via globally optimal subspaces
Data Mining and Knowledge Discovery
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We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.