Conference Proceeding
Extended SMART Algorithms for Non-negative Matrix Factorization .
01/2006;
In proceeding of: Artificial Intelligence and Soft Computing - ICAISC 2006, 8th International Conference, Zakopane, Poland, June 25-29, 2006, Proceedings
Source: DBLP
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Citations (0)
- Cited In (7)
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Article: SPACE: an algorithm to predict and quantify alternatively spliced isoforms using microarrays.
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ABSTRACT: Exon and exon+junction microarrays are promising tools for studying alternative splicing. Current analytical tools applied to these arrays lack two relevant features: the ability to predict unknown spliced forms and the ability to quantify the concentration of known and unknown isoforms. SPACE is an algorithm that has been developed to (1) estimate the number of different transcripts expressed under several conditions, (2) predict the precursor mRNA splicing structure and (3) quantify the transcript concentrations including unknown forms. The results presented here show its robustness and accuracy for real and simulated data.Genome biology 02/2008; 9(2):R46. · 6.63 Impact Factor -
Conference Proceeding: FastNMF: A fast monotonic fixed-point non-negative Matrix Factorization algorithm with high ease of use.
19th International Conference on Pattern Recognition (ICPR 2008), December 8-11, 2008, Tampa, Florida, USA; 01/2008 -
Article: Nonnegative matrix factorization with quadratic programming
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ABSTRACT: Nonnegative matrix factorization (NMF) solves the following problem: find such nonnegative matrices and that Y≅AX, given only Y∈RI×K and the assigned index J (K⪢I⩾J). Basically, the factorization is achieved by alternating minimization of a given cost function subject to nonnegativity constraints. In the paper, we propose to use quadratic programming (QP) to solve the minimization problems. The Tikhonov regularized squared Euclidean cost function is extended with a logarithmic barrier function (which satisfies nonnegativity constraints), and then using second-order Taylor expansion, a QP problem is formulated. This problem is solved with some trust-region subproblem algorithm. The numerical tests are performed on the blind source separation problems.Neurocomputing. 01/2008;
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