6,390 research outputs found
Sparse Graph-based Transduction for Image Classification
Motivated by the remarkable successes of Graph-based Transduction (GT) and
Sparse Representation (SR), we present a novel Classifier named Sparse
Graph-based Classifier (SGC) for image classification. In SGC, SR is leveraged
to measure the correlation (similarity) of each two samples and a graph is
constructed for encoding these correlations. Then the Laplacian eigenmapping is
adopted for deriving the graph Laplacian of the graph. Finally, SGC can be
obtained by plugging the graph Laplacian into the conventional GT framework. In
the image classification procedure, SGC utilizes the correlations, which are
encoded in the learned graph Laplacian, to infer the labels of unlabeled
images. SGC inherits the merits of both GT and SR. Compared to SR, SGC improves
the robustness and the discriminating power of GT. Compared to GT, SGC
sufficiently exploits the whole data. Therefore it alleviates the undercomplete
dictionary issue suffered by SR. Four popular image databases are employed for
evaluation. The results demonstrate that SGC can achieve a promising
performance in comparison with the state-of-the-art classifiers, particularly
in the small training sample size case and the noisy sample case
A comparative study of nonparametric methods for pattern recognition
The applied research discussed in this report determines and compares the correct classification percentage of the nonparametric sign test, Wilcoxon's signed rank test, and K-class classifier with the performance of the Bayes classifier. The performance is determined for data which have Gaussian, Laplacian and Rayleigh probability density functions. The correct classification percentage is shown graphically for differences in modes and/or means of the probability density functions for four, eight and sixteen samples. The K-class classifier performed very well with respect to the other classifiers used. Since the K-class classifier is a nonparametric technique, it usually performed better than the Bayes classifier which assumes the data to be Gaussian even though it may not be. The K-class classifier has the advantage over the Bayes in that it works well with non-Gaussian data without having to determine the probability density function of the data. It should be noted that the data in this experiment was always unimodal
Laplacian Support Vector Machines Trained in the Primal
In the last few years, due to the growing ubiquity of unlabeled data, much
effort has been spent by the machine learning community to develop better
understanding and improve the quality of classifiers exploiting unlabeled data.
Following the manifold regularization approach, Laplacian Support Vector
Machines (LapSVMs) have shown the state of the art performance in
semi--supervised classification. In this paper we present two strategies to
solve the primal LapSVM problem, in order to overcome some issues of the
original dual formulation. Whereas training a LapSVM in the dual requires two
steps, using the primal form allows us to collapse training to a single step.
Moreover, the computational complexity of the training algorithm is reduced
from O(n^3) to O(n^2) using preconditioned conjugate gradient, where n is the
combined number of labeled and unlabeled examples. We speed up training by
using an early stopping strategy based on the prediction on unlabeled data or,
if available, on labeled validation examples. This allows the algorithm to
quickly compute approximate solutions with roughly the same classification
accuracy as the optimal ones, considerably reducing the training time. Due to
its simplicity, training LapSVM in the primal can be the starting point for
additional enhancements of the original LapSVM formulation, such as those for
dealing with large datasets. We present an extensive experimental evaluation on
real world data showing the benefits of the proposed approach.Comment: 39 pages, 14 figure
Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation
In the traditional object recognition pipeline, descriptors are densely
sampled over an image, pooled into a high dimensional non-linear representation
and then passed to a classifier. In recent years, Fisher Vectors have proven
empirically to be the leading representation for a large variety of
applications. The Fisher Vector is typically taken as the gradients of the
log-likelihood of descriptors, with respect to the parameters of a Gaussian
Mixture Model (GMM). Motivated by the assumption that different distributions
should be applied for different datasets, we present two other Mixture Models
and derive their Expectation-Maximization and Fisher Vector expressions. The
first is a Laplacian Mixture Model (LMM), which is based on the Laplacian
distribution. The second Mixture Model presented is a Hybrid Gaussian-Laplacian
Mixture Model (HGLMM) which is based on a weighted geometric mean of the
Gaussian and Laplacian distribution. An interesting property of the
Expectation-Maximization algorithm for the latter is that in the maximization
step, each dimension in each component is chosen to be either a Gaussian or a
Laplacian. Finally, by using the new Fisher Vectors derived from HGLMMs, we
achieve state-of-the-art results for both the image annotation and the image
search by a sentence tasks.Comment: new version includes text synthesis by an RNN and experiments with
the COCO benchmar
A Spectral View of Adversarially Robust Features
Given the apparent difficulty of learning models that are robust to
adversarial perturbations, we propose tackling the simpler problem of
developing adversarially robust features. Specifically, given a dataset and
metric of interest, the goal is to return a function (or multiple functions)
that 1) is robust to adversarial perturbations, and 2) has significant
variation across the datapoints. We establish strong connections between
adversarially robust features and a natural spectral property of the geometry
of the dataset and metric of interest. This connection can be leveraged to
provide both robust features, and a lower bound on the robustness of any
function that has significant variance across the dataset. Finally, we provide
empirical evidence that the adversarially robust features given by this
spectral approach can be fruitfully leveraged to learn a robust (and accurate)
model.Comment: To appear at NIPS 201
Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization
This paper addresses image classification through learning a compact and
discriminative dictionary efficiently. Given a structured dictionary with each
atom (columns in the dictionary matrix) related to some label, we propose
cross-label suppression constraint to enlarge the difference among
representations for different classes. Meanwhile, we introduce group
regularization to enforce representations to preserve label properties of
original samples, meaning the representations for the same class are encouraged
to be similar. Upon the cross-label suppression, we don't resort to
frequently-used -norm or -norm for coding, and obtain
computational efficiency without losing the discriminative power for
categorization. Moreover, two simple classification schemes are also developed
to take full advantage of the learnt dictionary. Extensive experiments on six
data sets including face recognition, object categorization, scene
classification, texture recognition and sport action categorization are
conducted, and the results show that the proposed approach can outperform lots
of recently presented dictionary algorithms on both recognition accuracy and
computational efficiency.Comment: 36 pages, 12 figures, 11 table
Human Emotional Facial Expression Recognition
An automatic Facial Expression Recognition (FER) model with Adaboost face
detector, feature selection based on manifold learning and synergetic prototype
based classifier has been proposed. Improved feature selection method and
proposed classifier can achieve favorable effectiveness to performance FER in
reasonable processing time
Supervised Laplacian Eigenmaps with Applications in Clinical Diagnostics for Pediatric Cardiology
Electronic health records contain rich textual data which possess critical
predictive information for machine-learning based diagnostic aids. However many
traditional machine learning methods fail to simultaneously integrate both
vector space data and text. We present a supervised method using Laplacian
eigenmaps to augment existing machine-learning methods with low-dimensional
representations of textual predictors which preserve the local similarities.
The proposed implementation performs alternating optimization using gradient
descent. For the evaluation we applied our method to over 2,000 patient records
from a large single-center pediatric cardiology practice to predict if patients
were diagnosed with cardiac disease. Our method was compared with latent
semantic indexing, latent Dirichlet allocation, and local Fisher discriminant
analysis. The results were assessed using AUC, MCC, specificity, and
sensitivity. Results indicate supervised Laplacian eigenmaps was the highest
performing method in our study, achieving 0.782 and 0.374 for AUC and MCC
respectively. SLE showed an increase in 8.16% in AUC and 20.6% in MCC over the
baseline which excluded textual data and a 2.69% and 5.35% increase in AUC and
MCC respectively over unsupervised Laplacian eigenmaps. This method allows many
existing machine learning predictors to effectively and efficiently utilize the
potential of textual predictors
An Improved Naive Bayes Classifier-based Noise Detection Technique for Classifying User Phone Call Behavior
The presence of noisy instances in mobile phone data is a fundamental issue
for classifying user phone call behavior (i.e., accept, reject, missed and
outgoing), with many potential negative consequences. The classification
accuracy may decrease and the complexity of the classifiers may increase due to
the number of redundant training samples. To detect such noisy instances from a
training dataset, researchers use naive Bayes classifier (NBC) as it identifies
misclassified instances by taking into account independence assumption and
conditional probabilities of the attributes. However, some of these
misclassified instances might indicate usages behavioral patterns of individual
mobile phone users. Existing naive Bayes classifier based noise detection
techniques have not considered this issue and, thus, are lacking in
classification accuracy. In this paper, we propose an improved noise detection
technique based on naive Bayes classifier for effectively classifying users'
phone call behaviors. In order to improve the classification accuracy, we
effectively identify noisy instances from the training dataset by analyzing the
behavioral patterns of individuals. We dynamically determine a noise threshold
according to individual's unique behavioral patterns by using both the naive
Bayes classifier and Laplace estimator. We use this noise threshold to identify
noisy instances. To measure the effectiveness of our technique in classifying
user phone call behavior, we employ the most popular classification algorithm
(e.g., decision tree). Experimental results on the real phone call log dataset
show that our proposed technique more accurately identifies the noisy instances
from the training datasets that leads to better classification accuracy.Comment: The 15th Australasian Data Mining Conference (AusDM 2017), Melbourne,
Australi
Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)
A brain can detect outlier just by using only normal samples. Similarly,
one-class classification (OCC) also uses only normal samples to train the model
and trained model can be used for outlier detection. In this paper, a
multi-layer architecture for OCC is proposed by stacking various Graph-Embedded
Kernel Ridge Regression (KRR) based Auto-Encoders in a hierarchical fashion.
These Auto-Encoders are formulated under two types of Graph-Embedding, namely,
local and global variance-based embedding. This Graph-Embedding explores the
relationship between samples and multi-layers of Auto-Encoder project the input
features into new feature space. The last layer of this proposed architecture
is Graph-Embedded regression-based one-class classifier. The Auto-Encoders use
an unsupervised approach of learning and the final layer uses semi-supervised
(trained by only positive samples and obtained closed-form solution) approach
to learning. The proposed method is experimentally evaluated on 21 publicly
available benchmark datasets. Experimental results verify the effectiveness of
the proposed one-class classifiers over 11 existing state-of-the-art
kernel-based one-class classifiers. Friedman test is also performed to verify
the statistical significance of the claim of the superiority of the proposed
one-class classifiers over the existing state-of-the-art methods. By using two
types of Graph-Embedding, 4 variants of Graph-Embedded multi-layer KRR-based
one-class classifier has been presented in this paper. All 4 variants performed
better than the existing one-class classifiers in terms of various discussed
criteria in this paper. Hence, it can be a viable alternative for OCC task. In
the future, various other types of Auto-Encoders can be explored within
proposed architecture.Comment: arXiv admin note: substantial text overlap with arXiv:1805.0780
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