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Proceedings Article
Detecting emotions in social media: a constrained optimization approach
Yichen Wang,Aditya Pal +1 more
TL;DR: A constraint optimization framework to discover emotions from social media content of the users using several novel constraints such as emotion bindings, topic correlations, along with specialized features proposed by prior work and well-established emotion lexicons is proposed.
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Using score-informed constraints for NMF-based source separation
Sebastian Ewert,Meinard Müller +1 more
TL;DR: This paper presents an extended approach to non-negative matrix factorization that uses additional score information to guide the decomposition process and shows that using such double constraints results in musically meaningful decompositions similar to parametric approaches, while being computationally less demanding and easier to implement.
Journal ArticleDOI
Fuzzy Cognitive Diagnosis for Modelling Examinee Performance
TL;DR: A fuzzy cognitive diagnosis framework for examinees’ cognitive modelling with both objective and subjective problems is proposed, and extensive experiments on three real-world datasets prove that FuzzyCDF can reveal the knowledge states and cognitive level of the examinees effectively and interpretatively.
Proceedings ArticleDOI
Tensor dictionary learning with sparse TUCKER decomposition
Syed Zubair,Wenwu Wang +1 more
TL;DR: A new algorithm for dictionary learning based on tensor factorization using a TUCKER model, in which sparseness constraints are applied to the core tensor, of which the n-mode factors are learned from the input data in an alternate minimization manner using gradient descent.
Journal ArticleDOI
Non-negative matrix factorization: Ill-posedness and a geometric algorithm
TL;DR: The development of the geometric algorithm framework illustrates the ill-posedness of the NMF problem and suggests that NMF is not sufficiently constrained to be applied successfully outside of a particular class of problems.
Related Papers (5)
Learning the parts of objects by non-negative matrix factorization
Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values†
Pentti Paatero,Unto Tapper +1 more