© National Instruments Corporation 1 Machine Learning Toolkit
LabVIEW Machine Learning Toolkit
User Manual
1. INTRODUCTION ............................................................................................................................................. 1
2. FEATURES ...................................................................................................................................................... 1
2.1 MACHINE LEARNING ALGORITHMS ....................................................................................................................... 2
2.1.1 Unsupervised learning algorithms ........................................................................................................... 2
2.1.2 Supervised learning algorithms ............................................................................................................... 3
2.1.3 Dimension reduction algorithms .............................................................................................................. 4
2.2 VARIANT DATA TYPE .......................................................................................................................................... 5
2.3 DISTANCE/KERNEL VI REFERENCE ........................................................................................................................ 6
2.4 VALIDATION & VISUALIZATION UTILITIES ............................................................................................................... 6
3. SYSTEM REQUIREMENTS ............................................................................................................................... 6
4. INSTALLATION NOTES .................................................................................................................................... 6
TABLE I. THE APPLICABILITY OF VALIDATION AND VISUALIZATION UTILITIES TO DIFFERENT MACHINE LEARNING ALGORITHMS. “X”
INDICATES THAT A UTILITY IS APPLICABLE TO A CERTAIN ALGORITHM. ......................................................................................... 7
1. Introduction
The idea of machine learning is to mimic the learning process of human beings, i.e., gaining knowledge
through experience. Machine learning algorithms allow machines to generalize rules from empirical data,
and, based on the learned rules, make predictions for future data. The Machine Learning Toolkit (MLT)
provides various machine learning algorithms in LabVIEW. It is a powerful tool for problems such as
visualization of high-dimensional data, pattern recognition, function regression and cluster identification.
2. Features
The Machine Learning Toolkit includes algorithms, data types, validation functions, and visualization
tools.
© National Instruments Corporation 2 Machine Learning Toolkit
2.1 Machine Learning Algorithms
2.1.1 Unsupervised learning algorithms
Unsupervised learning refers to the problems of revealing hidden structure in unlabeled data.
Since the data are unlabeled, there is no error signal fed back to the learner in the algorithm.
This distinguishes unsupervised learning from supervised learning.
Clustering is one of the main and important approaches of unsupervised learning. Clustering
means the assignment of class memberships to a set of objects so that similar objects are
assigned into the same class and dissimilar ones are assigned into different classes. Each class
often represents a meaningful pattern in the respective problem. Clustering is thereby useful
for identification of different patterns in data. For example, in image processing clustering
can be used to divide a digital image into distinct regions for border detection or object
recognition.
List of functions:
k-means
k-medians
k-medoids
Fuzzy C-means
Gaussian Mixture Model (GMM)
Hierarchical Clustering
Spectral Clustering
Vector Quantization (VQ)
Self-Organizing Map (SOM)
Conceptual diagram of usage of the Machine Learning Toolkit for unsupervised learning
Data preparation
Data need to be formatted to fit the API of the unsupervised learning function the
user selects.
Unsupervised learning function application
An unsupervised learning function is used to learn the structure of the input data.
Evaluation/Visualization of results
Refer to Section 2.4 for the choice of appropriate evaluation/visualization utility.
Examples:
Example_Clustering
Example_SOM
Data
preparation
Unsupervised
learning function
Evaluation/
Visualization of
results
© National Instruments Corporation 3 Machine Learning Toolkit
2.1.2 Supervised learning algorithms
Supervised learning refers to the generalization of the relationship (function) between the
input data and their corresponding outputs (labels). The relationship (function) is learned
through a training set of examples, each of which is a pair of an input data and a desired
output. During the training, the error between the actual and the desired outputs is frequently
fed back into the system for tuning the system parameters according to certain learning rule.
The system “learns” by adapting itself to minimize the error. After the training, the
performance of the learned relationship (function) should be evaluated on a test set (of
examples) that is separate from the training set.
Supervised learning is useful for pattern recognition, function regression, etc. One example of
applications is recognition of handwritten numbers. A supervised classifier can be trained
with a reservoir of handwritten numbers, each with a label (the true number each image
represents). Having been validated on a separate test set, the trained classifier can be used for
fast and accurate recognition of future handwritten numbers.
List of functions:
k-Nearest Neighbors (k-NN)
Back-propagation (BP) Neural Network
Learning Vector Quantization (LVQ)
Support Vector Machine (SVM)
Conceptual diagram of usage of the MLT for supervised learning
Data preparation
Data need to be formatted to fit the API of the unsupervised learning function the
user selects.
Splitting data into training and test sets
Data
preparation
Supervised
learning
function
(learn w/
training set)
Evaluation/
Visualization
of results
Splitting data
into training
and test sets
Supervised
learning
function
(evaluate
w/ test set)
Training set
Test set
© National Instruments Corporation 4 Machine Learning Toolkit
The MLT provides a utility (Training & Test Set.vi) to split original data into a
training set and a test set with a user-specified ratio.
Supervised learning function (learn w/ training set)
The training set is used for the learning procedure.
Supervised learning function (evaluate w/ test set)
The test set is used for the evaluation of the performance.
Evaluation/Visualization of results
Refer to Section 2.4 for the choice of appropriate evaluation/visualization utility.
Examples:
Example_BP Network_Classification
Example_BP Network_Curve Fitting
Example_LVQ
Example_SVM
2.1.3 Dimension reduction algorithms
Dimension reduction refers to the process of reducing the number of dimension of the data.
The projection of the data set in the reduced space is often desired to preserve certain
important data characteristics. In some cases data analysis, such as clustering, can be done
more easily and accurately in the reduced space than in the original space. One prime
application of dimension reduction is face recognition, where face images represented by a
large number of pixels are projected to a more manageable low-dimensional “feature” space
before classification.
List of functions:
Isometric Feature Mapping (Isomap)
Locally Linear Embedding (LLE)
Multidimensional Scaling (MDS)
Principal Component Analysis (PCA)
Kernel PCA
Linear Discriminant Analysis (LDA)
Conceptual diagram of usage of the MLT for dimension reduction
Data
preparation
Dimension
reduction function
Evaluation/
Visualization of
results
© National Instruments Corporation 5 Machine Learning Toolkit
Data preparation
Data need to be formatted to fit the API of the unsupervised learning function the
user selects.
Dimension reduction function
A dimension reduction function is used to project the input data to a reduced space.
Evaluation/Visualization of results
Refer to Section 2.4 for the choice of appropriate evaluation/visualization utility.
Examples:
Example_Manifold learning
Example_LDA
Example_Kernel PCA
2.2 Variant Data Type
For learning algorithms that require an input data type to be numeric, the data needs to be
organized into a 2-D array of numeric numbers, where each row is an input sample. For learning
algorithms that utilize the (dis)similarity relationships of the data samples as inputs, data samples
can be any type of object. In this case, the input data needs to be organized into a 1-D array of
variants. In addition, the user needs to specify the distance/kernel VI to use. Refer to Section 2.3
for the distance/kernel VI provided by the MLT.
Functions for which input data is a 1-D array of variants and a reference to a distance/kernel
VI is a required input:
k-medoids
Hierarchical Clustering
Spectral Clustering
k-Nearest Neighbor (k-NN)
Isometric Feature Mapping (Isomap)
Locally Linear Embedding (LLE)
Multidimensional Scaling (MDS)
Examples:
Example_Clustering
Example_Manifold learning
© National Instruments Corporation 6 Machine Learning Toolkit
2.3 Distance/Kernel VI Reference
Some of the algorithms require the user to specify a distance/kernel VI. Refer to Section 2.2 for
the list of applicable functions. The MLT provides some of the most frequently-used distances
and kernel functions.
2.4 Validation & Visualization Utilities
The MLT provides validation and visualization utilities to facilitate the monitoring of the quality
of learning. The utilities fall into three categories: cluster validity indices, evaluation of
classification, visualization of learned results. The list of functions in each category is shown
below.
Cluster validity indices:
Rand Index
Davies-Bouldin (DB) Index
Jaccard Index
Dunn Index
Evaluation of classification:
Classification Accuracy
Confusion Matrix
Visualization of learned results:
Visualization (2D &3D)
Plot SOM (2D &3D)
The applicability of each function to different algorithms is shown in Table I.
3. System Requirements
Windows XP or later
LabVIEW 2009 or later
4. Installation Notes
Download and unzip the latest installer from NI Labs. Run Setup.exe.
Launch LabVIEW so that the installed menus can rebuild.
Open the diagram and go to Addons >> Machine Learning.
© National Instruments Corporation 7 Machine Learning Toolkit
Table I. The applicability of validation and visualization utilities to different machine learning
algorithms. “x” indicates that a utility is applicable to a certain algorithm.
Validation Utility Visualization Utility
Rand
Index
DB
Index
Dunn
Index
Jaccard
Index
Classification
Accuracy
Confusion
Matrix
Visualization
(2D &3D)
Plot SOM
(2D &3D)
A
lg
o
ri
th
m
U
n
su
p
er
v
is
ed
L
ea
rn
in
g
k-means x x x x x
k-medians x x x x x
k-medoids x x x x x
Gaussian
Mixture Model
x x x x x
Fuzzy Cmeans x x x x x
Hierarchical
Clustering
x x x x x
Spectral
Clustering
x x x x x
SOM x x
VQ x x x x x
S
u
p
er
v
is
e
d
L
ea
rn
in
g
k-NN x x x
LVQ x x x
SVM x x x
BP neural
network
x x x
D
im
en
si
o
n
R
ed
u
ct
io
n
Isomap x
LLE x
LDA x
MDS x
PCA x
Kernal PCA x
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