Sparse Recovery, Classification, and Data Compression via Improved Maximum Feasible Subsystem Algorithms
Bibliographic record
Abstract
Two new strategies are developed in this thesis to increase the speed of the state-ofthe-art Maximum Feasible Subsystem (MAX FS) algorithms.The newly developed strategies can be combined with any MAX FS algorithm to increase its speed while preserving or improving solution quality.The improved algorithms apply in the case of dense constraint matrices such as those found when various data compression/dimensionality reduction, classification, and sparse recovery problems are converted to MAX FS problems.This approach is used for sparse recovery in Compressive Sensing (CS) and data compression via Nonnegative Matrix Factorization (NNMF) for the first time in this thesis.In CS, the new algorithm can successfully recover real-world signals such as speech and Electrocardiogram (ECG) signals that have been more greatly compressed than existing methods can handle, and with greater recovered signal quality.They also deliver sparser solutions when compared with those obtained using traditional sparse recovery algorithms.The new algorithm significantly reduces the solution time of the existing MAX FS methods.It reduces the solution time of Method B by 80.64% and 77.82% for recovery of compressively sensed ECG and speech signals for instance, respectively.Three novel MAX FS-based methods for NNMF are developed and investigated using synthetic data and real-world image datasets.The new NNMF algorithms, unlike the majority of existing NNMF methods, do not require an initialization method or prior knowledge of the matrix rank.My PhD adventure transformed my life and broadened my perspective!One thing I learnt and felt the most is the great impact of incredible people around me in being who I am today.I am so grateful for having this opportunity to express my gratitude to some of those wonderful people, who provided their unconditional support during my PhD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".