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- Arto Klami, Seppo Virtanen, Samuel Kaski
- Journal of Machine Learning Research
- 2013

Canonical correlation analysis (CCA) is a classical method for seeking correlations between two multivariate data sets. During the last ten years, it has received more and more attention in the machine learning community in the form of novel computational formulations and a plethora of applications. We review recent developments in Bayesian models and… (More)

- Seppo Virtanen, Arto Klami, Suleiman A. Khan, Samuel Kaski
- AISTATS
- 2012

We introduce a factor analysis model that summarizes the dependencies between observed variable groups, instead of dependencies between individual variables as standard factor analysis does. A group may correspond to one view of the same set of objects, one of many data sets tied by co-occurrence, or a set of alternative variables collected from statistics… (More)

- Jaakko Peltonen, Arto Klami, Samuel Kaski
- Neural Networks
- 2004

We have earlier introduced a principle for learning metrics, which shows how metric-based methods can be made to focus on discriminative properties of data. The main applications are in supervising unsupervised learning to model interesting variation in data, instead of modeling all variation as plain unsupervised learning does. The metrics are derived by… (More)

- Arto Klami, Craig Saunders, Teófilo Emídio de Campos, Samuel Kaski
- Multimedia Information Retrieval
- 2008

Query formulation and efficient navigation through data to reach relevant results are undoubtedly major challenges for image or video retrieval. Queries of good quality are typically not available and the search process needs to rely on relevance feedback given by the user, which makes the search process iterative. Giving explicit relevance feedback is… (More)

- Arto Klami, Guillaume Bouchard, Abhishek Tripathi
- ArXiv
- 2013

CMF is a technique for simultaneously learning low-rank representations based on a collection of matrices with shared entities. A typical example is the joint modeling of useritem, item-property, and user-feature matrices in a recommender system. The key idea in CMF is that the embeddings are shared across the matrices, which enables transferring… (More)

- Seppo Virtanen, Yangqing Jia, Arto Klami, Trevor Darrell
- UAI
- 2012

Multi-modal data collections, such as corpora of paired images and text snippets, require analysis methods beyond single-view component and topic models. For continuous observations the current dominant approach is based on extensions of canonical correlation analysis, factorizing the variation into components shared by the different modalities and those… (More)

- Arto Klami
- 2004

Data discretization is needed for various reasons. One reason is that there are many machine learning algorithms that can only be applied to discrete data. In order to use those algorithms, we need to discretize the data. We might also want to do that for solely computational reasons; some problems are easier to compute for discrete variables. Finally, if… (More)

- Arto Klami, Samuel Kaski
- Neurocomputing
- 2008

We study data fusion under the assumption that data source-specific variation is irrelevant and only shared variation is relevant. Traditionally the shared variation has been sought by maximizing a dependency measure, such as correlation of linear projections in Canonical Correlation Analysis. In this traditional framework it is hard to tackle overfitting… (More)

- Zakria Hussain, Arto Klami, +6 authors John Shawe-Taylor
- WAPA
- 2010

This paper describes Pinview, a contentbased image retrieval system that exploits implicit relevance feedback during a search session. The goal is to retrieve interesting images and the relevance feedback could be eye movements or clicks on the images. Pinview contains several novel methods that infer the intent of the user. From relevance feedback and… (More)

- Seppo Virtanen, Arto Klami, Samuel Kaski
- ICML
- 2011

Bayesian treatments of Canonical Correlation Analysis (CCA) -type latent variable models have been recently proposed for coping with overfitting in small sample sizes, as well as for producing factorizations of the data sources into correlated and non-shared effects. However, all of the current implementations of Bayesian CCA and its extensions are… (More)