35 research outputs found
Object Feature Extraction of Songket Image Using Chain Code Algorithm
The study was aimed at determining the feature of a motif found in a Songket image in order to make the object detectable and readable. The method used was image color segmentation in the form of a process of segmentation of the image area based on the similarity in colors, which was continued with the binary process that aims to change the image into binary form (0 and 1), so that it only has two colors namely black and white. This study also used mathematical morphology in detecting objects, by using dilation operation and filling holes. After the process of mathematical morphology was completed, the next process was motif extraction by applying moore contour tracking algorithms and the development of chain code algorithms. The results of the process carried out showed that the development chain code algorithm can generate the number of objects, the length of chain code, and probable value of rate of appearances of each chain code in a motif, despite there are some objects in a motif. Then the values are stored into the database as The Feature of Songket Motifs
Unsupervised Feature Selection with Adaptive Structure Learning
The problem of feature selection has raised considerable interests in the
past decade. Traditional unsupervised methods select the features which can
faithfully preserve the intrinsic structures of data, where the intrinsic
structures are estimated using all the input features of data. However, the
estimated intrinsic structures are unreliable/inaccurate when the redundant and
noisy features are not removed. Therefore, we face a dilemma here: one need the
true structures of data to identify the informative features, and one need the
informative features to accurately estimate the true structures of data. To
address this, we propose a unified learning framework which performs structure
learning and feature selection simultaneously. The structures are adaptively
learned from the results of feature selection, and the informative features are
reselected to preserve the refined structures of data. By leveraging the
interactions between these two essential tasks, we are able to capture accurate
structures and select more informative features. Experimental results on many
benchmark data sets demonstrate that the proposed method outperforms many state
of the art unsupervised feature selection methods
Online Unsupervised Multi-view Feature Selection
In the era of big data, it is becoming common to have data with multiple
modalities or coming from multiple sources, known as "multi-view data".
Multi-view data are usually unlabeled and come from high-dimensional spaces
(such as language vocabularies), unsupervised multi-view feature selection is
crucial to many applications. However, it is nontrivial due to the following
challenges. First, there are too many instances or the feature dimensionality
is too large. Thus, the data may not fit in memory. How to select useful
features with limited memory space? Second, how to select features from
streaming data and handles the concept drift? Third, how to leverage the
consistent and complementary information from different views to improve the
feature selection in the situation when the data are too big or come in as
streams? To the best of our knowledge, none of the previous works can solve all
the challenges simultaneously. In this paper, we propose an Online unsupervised
Multi-View Feature Selection, OMVFS, which deals with large-scale/streaming
multi-view data in an online fashion. OMVFS embeds unsupervised feature
selection into a clustering algorithm via NMF with sparse learning. It further
incorporates the graph regularization to preserve the local structure
information and help select discriminative features. Instead of storing all the
historical data, OMVFS processes the multi-view data chunk by chunk and
aggregates all the necessary information into several small matrices. By using
the buffering technique, the proposed OMVFS can reduce the computational and
storage cost while taking advantage of the structure information. Furthermore,
OMVFS can capture the concept drifts in the data streams. Extensive experiments
on four real-world datasets show the effectiveness and efficiency of the
proposed OMVFS method. More importantly, OMVFS is about 100 times faster than
the off-line methods
A Matlab Toolbox for Feature Importance Ranking
More attention is being paid for feature importance ranking (FIR), in
particular when thousands of features can be extracted for intelligent
diagnosis and personalized medicine. A large number of FIR approaches have been
proposed, while few are integrated for comparison and real-life applications.
In this study, a matlab toolbox is presented and a total of 30 algorithms are
collected. Moreover, the toolbox is evaluated on a database of 163 ultrasound
images. To each breast mass lesion, 15 features are extracted. To figure out
the optimal subset of features for classification, all combinations of features
are tested and linear support vector machine is used for the malignancy
prediction of lesions annotated in ultrasound images. At last, the
effectiveness of FIR is analyzed according to performance comparison. The
toolbox is online (https://github.com/NicoYuCN/matFIR). In our future work,
more FIR methods, feature selection methods and machine learning classifiers
will be integrated