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Emergence of Object-Selective Features in Unsupervised Feature Learning
2012
Neural Information Processing Systems
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Recent work in unsupervised feature learning has focused on the goal of discovering high-level features from unlabeled images. ...
In this paper, we aim to test the hypothesis that unsupervised feature learning methods, provided with only unlabeled data, can learn high-level, invariant features that are sensitive to commonly-occurring ...
As a result, it is very unclear where an "object class" begins or ends in this type of patch dataset, and less clear that a completely unsupervised learning algorithm could manage to cre-ate "object-selective ...
dblp:conf/nips/CoatesKN12
fatcat:2qe4q76y4vffreducefpkjdj3u
Reconstruction-based Unsupervised Feature Selection: An Embedded Approach
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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In this paper, we investigate how to learn the reconstruction function from the data automatically for unsupervised feature selection, and propose a novel reconstruction-based unsupervised feature selection ...
Recently, data reconstruction error emerged as a new criterion for unsupervised feature selection, which defines feature relevance as the capability of features to approximate original data via a reconstruction ...
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ...
doi:10.24963/ijcai.2017/300
dblp:conf/ijcai/LiTL17
fatcat:l7qkumbruncxlfjcil5nuojigq
Object-Centric Representation Learning from Unlabeled Videos
[article]
2016
arXiv
pre-print
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In this work, we explore unsupervised feature learning from unlabeled video. ...
restriction in the scope of data relevant for learning. ...
Object-Centric Representation Learning from Unlabeled Videos ...
arXiv:1612.00500v1
fatcat:p4bnb4p2ubgvjkiizpicex4sti
Selective unsupervised feature learning with Convolutional Neural Network (S-CNN)
2016
2016 23rd International Conference on Pattern Recognition (ICPR)
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2016 pre-print arXiv:1606.02210v1 fatcat:szoedoqliffatjsnhjfsx6ryxa
This method for unsupervised feature learning is then successfully applied to a challenging object recognition task. ...
Selective Convolutional Neural Network (S-CNN) is a simple and fast algorithm, it introduces a new way to do unsupervised feature learning, and it provides discriminative features which generalize well ...
In Figure 1 we show the overview of algorithm. Selective search finds the important parts of the object. Then CNN learns the features to classify those important parts. ...
doi:10.1109/icpr.2016.7900009
dblp:conf/icpr/GhaderiA16
fatcat:7kkko462vrepfjpxamwct6nmlm
Dimensionality Reduction: An Effective Technique for Feature Selection
2015
International Journal of Computer Applications
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By reducing the unrelated (irrelevant) and unnecessary features related to data, or by means of effectively merging original features to produce a smaller set of feature with more discriminative control ...
It has been observed that most of the time dataset is multidimensional and larger in size. ...
In machine learning and data mining organizations suffers addressed problems of feature selection, especially in supervised and unsupervised models, the topic of several mechanisms [5] . ...
doi:10.5120/20535-2893
fatcat:f2uzmepx2fbgfi5hug5d4gzn7y
Zero-shot Feature Selection via Exploiting Semantic Knowledge
[article]
2020
arXiv
pre-print
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Feature selection plays an important role in pattern recognition and machine learning systems. Supervised knowledge can significantly improve the performance. ...
Therefore, this paper studies the problem of Zero-Shot Feature Selection, i.e., building a feature selection model that generalizes well to "unseen" concepts with limited training data of "seen" concepts ...
FSASL [10] is an unsupervised method performing structure learning and feature selection simultaneously. [33] is the classical supervised feature selection method. 5. ...
arXiv:1908.03464v2
fatcat:hbcnjl4lnbexrgmbx2z6codg7m
A Survey on Unsupervised Machine Learning Algorithms for Automation, Classification and Maintenance
2015
International Journal of Computer Applications
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Sparse coding learns hierarchical feature representations from raw RGB-D data in an unsupervised way by using hierarchical matching pursuit. ...
Unsupervised learning techniques are used for learn complex, highly non-linear models with millions parameters to used large amount of unlabeled data. ...
area of feature subset selection: unsupervised learning is quite young. ...
doi:10.5120/21131-4058
fatcat:aq6hlihqizcehjcajdgnx7a2ti
A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning
2019
Proceedings of the 2019 8th International Conference on Educational and Information Technology - ICEIT 2019
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In this paper, the effect of feature selection in malware detection using machine learning techniques is studied. ...
We employ supervised and unsupervised machine learning algorithms with and without feature selection. These include both classification and clustering algorithms. ...
Furthermore, we carried out supervised learning without feature selection. Finally, unsupervised learning without feature selection was done. ...
doi:10.1145/3318396.3318448
fatcat:tdep7jy24rhwvh3mbzoh7p4hom
Subitizing with Variational Autoencoders
[article]
2018
arXiv
pre-print
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In computer vision, it has been shown that numerosity emerges as a statistical property in neural networks during unsupervised learning from simple synthetic images. ...
Numerosity, the number of objects in a set, is a basic property of a given visual scene. ...
The contributions of this work are the following. We explore the emergence of visual number sense in deep networks trained in an unsupervised setting on natural images. ...
arXiv:1808.00257v1
fatcat:glevffunrvcdbf6yaxhtm3clq4
Symbols as Self-emergent Entities in an Optimization Process of Feature Extraction and Predictions
2006
Biological cybernetics
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In the mammalian cortex the early sensory processing can be characterized as feature extraction resulting in local and analogue low-level representations. ...
Secondly, addressing a stage following early visual processing we suggest to extend the unsupervised learning scheme by including predictive processes. ...
This allows a substantial part of feature selectivity in early visual processing to be, be understood on the basis of unsupervised learning according to a small number of objectives. ...
doi:10.1007/s00422-006-0050-3
pmid:16496197
fatcat:kxrsgxoqwvarfpdzv3vy44vz7a
Unsupervised Category Discovery in Images Using Sparse Neural Coding
2007
Procedings of the British Machine Vision Conference 2007
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We present an unsupervised method for learning and recognizing object categories from unlabeled images. ...
In recognition, this model is used in a maximum-likelihood manner to classify unseen images, and we find units emerging from learning that respond selectively to specific image categories. ...
We thank Thomas Serre and Minjoon Kouh of MIT for providing the visual system model used here as well as assistance with its operation, and Richard Murray, Jerry Marsden, and Pietro Perona at Caltech and ...
doi:10.5244/c.21.102
dblp:conf/bmvc/WaydoK07
fatcat:fi3s4kz2ffbgzko66w3yhyxpme
Measures for unsupervised fuzzy-rough feature selection
2010
International Journal of Hybrid Intelligent Systems
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In this paper, some new fuzzy-rough set-based approaches to unsupervised feature selection are proposed. ...
For supervised learning, feature selection algorithms attempt to maximise a given function of predictive accuracy. ...
Acknowledgement The authors would like to acknowledge the financial support for this research through The Research institute of Visual Computing -Wales (RIVIC) . ...
doi:10.3233/his-2010-0118
fatcat:7kl3bbnfcndsjiwt4ydjh4mbgy
Measures for Unsupervised Fuzzy-Rough Feature Selection
2009
2009 Ninth International Conference on Intelligent Systems Design and Applications
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In this paper, some new fuzzy-rough set-based approaches to unsupervised feature selection are proposed. ...
For supervised learning, feature selection algorithms attempt to maximise a given function of predictive accuracy. ...
Acknowledgement The authors would like to acknowledge the financial support for this research through The Research institute of Visual Computing -Wales (RIVIC) . ...
doi:10.1109/isda.2009.45
dblp:conf/isda/MacParthalainJ09
fatcat:ovaugcbn35dqpdd2l26j3vw6ia
Sparse Nonlinear Feature Selection Algorithm via Local Structure Learning
2019
Emerging Science Journal
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In this paper, we propose a new unsupervised feature selection algorithm by considering the nonlinear and similarity relationships within the data. ...
Specifically, we use a kernel function to map each feature of the data into the kernel space. ...
In the unsupervised feature selection algorithm of the past two years, Almusallam et al. proposed an efficient unsupervised feature selection for streaming features [8] . ...
doi:10.28991/esj-2019-01175
fatcat:sdicjem7rnc7tky36wqjrqnvze
Machine Learning-Based State-Of-The-Art Methods For The Classification Of RNA-Seq Data
[article]
2017
bioRxiv
pre-print
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In this chapter, we are going to discuss various machine learning approaches for RNA-Seq data classification and their implementation. ...
Advancements in bioinformatics, along with developments in machine learning based classification, would provide powerful toolboxes for classifying transcriptome information available through RNA-Seq data ...
For a Big Data feature selection process such as in case of RNA-Seq data, both supervised learning and unsupervised learning can be implemented to make decision. ...
doi:10.1101/120592
fatcat:frdzqa4awvbuddkp4vxmyvyo2q
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