reppi
A Python library for representation learning — sparse coding and dictionary learning algorithms implemented close to their original formulations.
Installation
pip install reppi
Algorithms
| Algorithm | Class | Reference |
|---|---|---|
| Orthogonal Matching Pursuit | OMP |
Elad et al., 2008 |
| K-SVD | KSVD |
Aharon et al., 2006 |
| Label Consistent K-SVD | LCKSVD |
Jiang et al., 2011 |
| Frozen Dictionary Learning | IncrementalFrozenDictionary |
Carroll et al., 2017 |
Convention
reppi follows the column-major convention common in the sparse representation literature: signals are columns, so data matrices are shaped (n_features, n_samples). This matches the MATLAB toolboxes the implementations are based on. If your data is in sklearn's (n_samples, n_features) layout, transpose before passing it in.
Quick Start
Sparse Coding with OMP
from reppi import OMP
import numpy as np
# D: (n_features, n_atoms), unit-norm columns
# X: (n_features, n_samples)
omp = OMP(n_nonzero_coefs=10)
Gamma = omp.encode(X, D) # (n_atoms, n_samples)
Dictionary Learning with K-SVD
from reppi import KSVD
ksvd = KSVD(
n_components=128, # number of atoms
n_nonzero_coefs=10, # sparsity level T
n_iter=20,
verbose=True,
)
ksvd.fit(X_train) # X_train: (n_features, n_samples)
D = ksvd.D_ # learned dictionary (n_features, n_components)
Gamma = ksvd.transform(X) # sparse codes (n_components, n_samples)
Discriminative Dictionary Learning with LC-KSVD
LC-KSVD jointly learns a dictionary and a linear classifier from labelled data. Labels are passed as a one-hot matrix H of shape (n_classes, n_samples).
LC-KSVD1 — reconstruction + label-consistency:
from reppi import LCKSVD
model = LCKSVD(
n_components=570,
n_nonzero_coefs=30,
alpha=4.0, # weight for label-consistency term
variant="lcksvd1",
n_iter=50,
n_iter_init=20, # K-SVD iterations for initialisation
verbose=True,
)
model.fit(X_train, H_train)
Gamma = model.transform(X_test) # (n_components, n_samples)
LC-KSVD2 — reconstruction + label-consistency + classification error:
model = LCKSVD(
n_components=570,
n_nonzero_coefs=30,
alpha=4.0,
beta=2.0, # weight for classifier term
variant="lcksvd2",
n_iter=50,
n_iter_init=20,
verbose=True,
)
model.fit(X_train, H_train)
predictions = model.predict(X_test) # integer class indices
accuracy = model.score(X_test, H_test) # float in [0, 1]
References
- M. Aharon, M. Elad, A. Bruckstein. "The K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation". IEEE Trans. Signal Processing, 54(11), 2006.
- M. Elad, R. Rubinstein, M. Zibulevsky. "Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit". Technion Technical Report, 2008.
- Z. Jiang, Z. Lin, L. Davis. "Learning A Discriminative Dictionary for Sparse Coding via Label Consistent K-SVD". CVPR, 2011.
- B. T. Carroll, B. M. Whitaker, W. Daley, D. V. Anderson. "Outlier Learning via Augmented Frozen Dictionaries". IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2017.
1 maintainer
Metadata
1 maintainer
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