Incremental linear discriminant analysis for classification of data streams

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Online Continual Learning in Image Classification: An Empirical Survey (2021, methodology): “DSLDA [124] freezes the feature extractor and uses deep Streaming Linear Discriminant Analysis [125] to train the output layer incrementally.”
Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis (2019, methodology): “Especially relevant to this paper are SLDA [40] and Streaming Quadratic Discriminant Analysis (SQDA).”
A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020, methodology): “D-SLDA [15] deﬁnes the model as M 𝑡 ≡ 𝐹 ( 𝐺 ( ⋅ )) where is the ﬁxed upper part of the network and 𝐹 ( ⋅ ) is the output layer.”
Continual Learning with Foundation Models: An Empirical Study of Latent Replay (2022, background): “The total compute of SLDA is then roughly: .”
Class-Incremental Learning with Generative Classifiers (2021, methodology): “Incremental linear discriminant analysis [23, 40] is a popular method in the data mining community that is suitable for class-incremental learning.”
Evolving Fuzzy-Rule-Based Classifiers From Data Streams (2008, background): “, incremental versions of Bayesian classifiers [10], linear discriminant analysis (iLDA) [33], principal component analysis (iPCA) [33].”
Continual Learning in Deep Networks: an Analysis of the Last Layer (2021, methodology): “The online version of this algorithm is streaming linear discriminant analysis (SLDA) [29].”