Machine Learning for Beginners in Python: Dimensionality Reduction On Sparse Feature Matrix

Dimensionality Reduction On Sparse Feature Matrix


/* Load libraries */
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import TruncatedSVD
from scipy.sparse import csr_matrix
from sklearn import datasets
import numpy as np

Load Digits Data And Make Sparse

/* Load the data */
digits = datasets.load_digits()

/* Standardize the feature matrix */
X = StandardScaler().fit_transform(

/* Make sparse matrix */
X_sparse = csr_matrix(X)

Create Truncated Singular Value Decomposition

/* Create a TSVD */
tsvd = TruncatedSVD(n_components=10)

Run Truncated Singular Value Decomposition

/* Conduct TSVD on sparse matrix */
X_sparse_tsvd =

View Results

/* Show results */
print('Original number of features:', X_sparse.shape[1])
print('Reduced number of features:', X_sparse_tsvd.shape[1])
Original number of features: 64
Reduced number of features: 10

View Percent Of Variance Explained By New Features

/* Sum of first three components' explained variance ratios */


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