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A Survey on Transfer Learning
The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed. Expand
Domain Adaptation via Transfer Component Analysis
This work proposes a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation and proposes both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce thedistance between domain distributions by projecting data onto the learned transfer components. Expand
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
A new supervised dimensionality reduction algorithm called marginal Fisher analysis is proposed in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizing the interclass separability. Expand
Top 10 algorithms in data mining
This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN,Expand
Boosting for transfer learning
This paper presents a novel transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms and shows that this method can allow us to learn an accurate model using only a tiny amount of new data and a large amount of old data, even when the new data are not sufficient to train a model alone. Expand
One-Class Collaborative Filtering
This paper considers the one-class problem under the CF setting, and proposes two frameworks to tackle OCCF, one based on weighted low rank approximation; the other based on negative example sampling. Expand
Cross-domain sentiment classification via spectral feature alignment
This work develops a general solution to sentiment classification when the authors do not have any labels in a target domain but have some labeled data in a different domain, regarded as source domain and proposes a spectral feature alignment (SFA) algorithm to align domain-specific words from different domains into unified clusters, with the help of domain-independent words as a bridge. Expand
BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies
BOOST has identified some disease-associated interactions between genes in the major histocompatibility complex region in the type 1 diabetes data set and can serve as a computationally and statistically useful tool in the coming era of large-scale interaction mapping in genome-wide case-control studies. Expand
Transfer Learning via Dimensionality Reduction
A new dimensionality reduction method is proposed to find a latent space, which minimizes the distance between distributions of the data in different domains in a latentspace, which can be treated as a bridge of transferring knowledge from the source domain to the target domain. Expand
Mining high utility itemsets
A new pruning strategy based on utilities that allow pruning of low utility itemsets to be done by means of a weaker but antimonotonic condition is developed and shows that it does not require a user specified minimum utility and hence is effective in practice. Expand