A Memetic Heuristic for the Co-clustering Problem

Khoshneshin, Mohammad; Ghazizadeh, Mahtab; Street, W. Nick; Ohlmann, Jeffrey W.

doi:10.4108/icst.bict.2014.257884

Cited by 1 publication

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“…In this work we proposed an online algorithm for collaborative filtering [59] using the algorithm we presented in [58]. A few published approaches have addressed the incremental CF problem.…”

Section: Incremental Collaborative Filteringmentioning

confidence: 99%

Latent feature networks for statistical relational learning

Street

Khoshneshin

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In this dissertation, I explored relational learning via latent variable models. Traditional machine learning algorithms cannot handle many learning problems where there is a need for modeling both relations and noise. Statistical relational learning approaches emerged to handle these applications by incorporating both relations and uncertainties in these problems. Latent variable models are one of the successful approaches for statistical relational learning. These models assume a latent variable for each entity and then the probability distribution over relationships between entities is modeled via a function over latent variables. One important example of relational learning via latent variables is text data modeling. In text data modeling, we are interested in modeling the relationship between words and documents. Latent variable models learn this data by assuming a latent variable for each word and document. The co-occurrence value is defined as a function of these random variables. For modeling co-occurrence data in general (and text data in particular), we proposed latent logistic allocation (LLA). LLA outperforms the-state-of-the-art model-latent Dirichlet allocation-in text data modeling, document categorization and information retrieval. We also proposed query-based visualization which embeds documents relevant to a query in a 2-dimensional space. Additionally, I used latent variable models for other single-relational problems such as collaborative filtering and educational data mining. To move towards multi-relational learning via latent variable models, we propose latent feature networks (LFN). Multi-relational learning approaches model mul-I would like to express my sincere appreciation to my parents, Sima Sharifi and Rahim Khoshneshin. Their unconditional love, belief, and support has always given me courage and direction. I am in their debt for all I have or will have in life. I also would like to thank my brothers, Puya and Milad, and my parents-in-law, Mehrnaz Yamini and Ali Akbar Ghazizadeh, for their support and love. Last but not least, I wish to thank my beloved wife, Mahtab, for her love, patience, and support during these years. She encouraged me when I was hopeless and supported me when I was frustrated. I truly believe this dissertation could not have been possible without her by my side.

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Section: Incremental Collaborative Filteringmentioning

confidence: 99%

Latent feature networks for statistical relational learning

Street

Khoshneshin

Self Cite

View full text Add to dashboard Cite

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A Memetic Heuristic for the Co-clustering Problem

Cited by 1 publication

References 12 publications

Latent feature networks for statistical relational learning

Latent feature networks for statistical relational learning

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