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Logistic Regression Confined by Cardinality-Constrained Sample and Feature Selection
- Source :
- IEEE Trans Pattern Anal Mach Intell
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- Many vision-based applications rely on logistic regression for embedding classification within a probabilistic context, such as recognition in images and videos or identifying disease-specific image phenotypes from neuroimages. Logistic regression, however, often performs poorly when trained on data that is noisy , has irrelevant features , or when the samples are distributed across the classes in an imbalanced setting ; a common occurrence in visual recognition tasks. To deal with those issues, researchers generally rely on ad-hoc regularization techniques or model a subset of these issues. We instead propose a mathematically sound logistic regression model that selects a subset of (relevant) features and (informative and balanced) set of samples during the training process. The model does so by applying cardinality constraints (via $\ell _0$ l 0 -‘norm’ sparsity) on the features and samples. $\ell _0$ l 0 defines sparsity in mathematical settings but in practice has mostly been approximated (e.g., via $\ell _1$ l 1 or its variations) for computational simplicity. We prove that a local minimum to the non-convex optimization problems induced by cardinality constraints can be computed by combining block coordinate descent with penalty decomposition. On synthetic, image recognition, and neuroimaging datasets, we show that the accuracy of the method is higher than alternative methods and classifiers commonly used in the literature.
- Subjects :
- Adult
Male
Optimization problem
Databases, Factual
Computer science
Feature extraction
HIV Infections
Neuroimaging
Feature selection
02 engineering and technology
Logistic regression
Article
Cardinality
Artificial Intelligence
Image Processing, Computer-Assisted
0202 electrical engineering, electronic engineering, information engineering
Humans
Coordinate descent
business.industry
Applied Mathematics
Probabilistic logic
Brain
Reproducibility of Results
Pattern recognition
Middle Aged
Magnetic Resonance Imaging
Alcoholism
Logistic Models
ComputingMethodologies_PATTERNRECOGNITION
Computational Theory and Mathematics
Norm (mathematics)
Embedding
Female
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
business
Algorithms
Software
Subjects
Details
- ISSN :
- 19393539 and 01628828
- Volume :
- 42
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Accession number :
- edsair.doi.dedup.....4ceb28403ce9d05f0cd7639169eb6886