reppi

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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

Installaion

pip install reppi

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.

Key dates

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  • Released:
Newer release available (1.0.0)

1 maintainer

PyPI data
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Avatar for chamudithak from gravatar.com chamudithak

Requires

Python >=3.12

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