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In this paper, the authors propose incremental formulation of EDA to avoid learning from scratch. The proposed incremental algorithm takes less computation time and memory. Experiments are performed on three publicly available face datasets. Experimental results demonstrate the effectiveness of the proposed incremental formulation in comparison to its batch formulation in terms of computation time and memory requirement. Also, the proposed incremental algorithms (IEDA, DEDA) outperform incremental formulation of LDA in terms of classification accuracy.<\/p>","DOI":"10.4018\/ijcvip.2014010104","type":"journal-article","created":{"date-parts":[[2014,8,1]],"date-time":"2014-08-01T16:21:20Z","timestamp":1406910080000},"page":"40-55","source":"Crossref","is-referenced-by-count":1,"title":["Incremental and Decremental Exponential Discriminant Analysis for Face Recognition"],"prefix":"10.4018","volume":"4","author":[{"given":"Nitin","family":"Kumar","sequence":"first","affiliation":[{"name":"National Institute of Technology, Uttarakhand, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.K.","family":"Agrawal","sequence":"additional","affiliation":[{"name":"School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajay","family":"Jaiswal","sequence":"additional","affiliation":[{"name":"S. S. 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