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Subset Selection and Summarization in Sequential Data
2017
Neural Information Processing Systems
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Experiments on synthetic and real data, including instructional video summarization, show that our sequential subset selection framework not only achieves better encoding and diversity than the state of ...
In this paper, we develop a new framework for sequential subset selection that finds a set of representatives compatible with the dynamic models of data. ...
Acknowledgements This work is supported by NSF IIS-1657197 award and startup funds from the Northeastern University, College of Computer and Information Science. ...
dblp:conf/nips/ElhamifarK17
fatcat:zmvpdwaerfacdoodsnphd5q674
Diverse Sequential Subset Selection for Supervised Video Summarization
2014
Neural Information Processing Systems
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Whereas prior approaches, largely unsupervised in nature, focus on sampling useful frames and assembling them as summaries, we consider video summarization as a supervised subset selection problem. ...
To this end, we propose the sequential determinantal point process (seqDPP), a probabilistic model for diverse sequential subset selection. ...
C. and F. S. are partially supported by DARPA D11-AP00278, NSF IIS-1065243, and ARO #W911NF-12-1-0241. K. G. is supported by ONR YIP Award N00014-12-1-0754 and gifts from Intel and Google. B. ...
dblp:conf/nips/GongCGS14
fatcat:q2sjg3ugu5f2ta2bjx4ldywuu4
k-SDPP: Fixed-Size Video Summarization via Sequential Determinantal Point Processes
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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With the explosive growth of video data, video summarization which converts long-time videos to key frame sequences has become an important task in information retrieval and machine learning. ...
Moreover, an efficient branch and bound method (BB) considering sequential nature of the frames is provided to optimally select k frames delegating the summary from the divided segments. ...
For video summarization, we need to select a subset of all frames in a video over a DPP. And now we have some training frames in the form of videos and the ground-truth summaries. ...
doi:10.24963/ijcai.2020/108
dblp:conf/ijcai/ZhengL20
fatcat:gwreyv6mlfcnvknhabnptknity
An improved feature selection approach for chronic heart disease detection
2021
Bulletin of Electrical Engineering and Informatics
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In this study, sequential feature selection (SFS) algorithm is implemented for improving the classifier performance on heart disease detection by removing irrelevant features and training a model on optimal ...
Sequential feature selection (SFS) is successful algorithm to improve the performance of classification model on heart disease detection and reduces the computational time complexity. ...
ACKNOWLEDGEMENTS I would like to express my special thanks of gratitude to my wife Aster Belay who helped me a lot in typesetting and writing this manuscript. ...
doi:10.11591/eei.v10i6.3001
fatcat:553g6lljpbepxbjhcdetfof5dm
Video Summarization with Long Short-Term Memory
[chapter]
2016
Lecture Notes in Computer Science
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There, our main idea is to exploit auxiliary annotated video summarization datasets, in spite of their heterogeneity in visual styles and contents. ...
In addition to advances in modeling techniques, we introduce a strategy to address the need for a large amount of annotated data for training complex learning approaches to summarization. ...
Acknowledgements KG is partially supported by NSF IIS-1514118 and a gift from Intel. Others are partially supported by USC Graduate Fellowships, NSF IIS-1451412, 1513966, CCF-1139148 and A. P. ...
doi:10.1007/978-3-319-46478-7_47
fatcat:q2ajdbxa3zg2potmmja2ofzvni
A Review on Dimensionality Reduction Techniques
2017
International Journal of Computer Applications
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Progress in digital data acquisition and storage technology has resulted in exponential growth in high dimensional data. ...
Feature selection and feature extraction techniques as a preprocessing step are used for reducing data dimensionality. ...
Fig 3: Hybrid Model for Feature Selection The features of these three methods are summarized in table 1 given below -Two phase local selection -Sequential forward selection Some of the algorithms for feature ...
doi:10.5120/ijca2017915260
fatcat:2sfd5rzh6bafnpsswnedtugdh4
Orthogonal Forward Selection and Backward Elimination Algorithms for Feature Subset Selection
2004
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
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Sequential forward selection (SFS) and sequential backward elimination (SBE) are two commonly used search methods in feature subset selection. ...
The basic idea of the orthogonal feature subset selection algorithms is to find an orthogonal space in which to express features and to perform feature subset selection. ...
Orthogonal Backward Elimination Feature Subset Selection Procedure The OBE algorithm that combines Givens transform and sequential backward elimination algorithm can be summarized as follows. i) Initially ...
doi:10.1109/tsmcb.2002.804363
pmid:15369099
fatcat:bgabazcm7bek3hepbud55zjh4e
Video Summarization with Long Short-term Memory
[article]
2016
arXiv
pre-print
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In particular, we show that it is crucial to take into consideration the sequential structures in videos and model them. ...
We propose a novel supervised learning technique for summarizing videos by automatically selecting keyframes or key subshots. ...
In contrast, LSTMs can model dependencies with a data-dependent on/off switch, which is extremely powerful for modeling sequential data [20] . ...
arXiv:1605.08110v2
fatcat:4fhu67gctfh2jpge3mfmpgnb4y
Sequential Facility Location: Approximate Submodularity and Greedy Algorithm
2019
International Conference on Machine Learning
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We develop and analyze a novel utility function and a fast optimization algorithm for subset selection in sequential data that incorporates the dynamic model of data. ...
We exploit the sequential structure of the problem and develop an efficient dynamic programming-based algorithm that computes the marginal gain exactly. ...
Acknowledgements This work is partially supported by grants from NSF (IIS-1657197), DARPA Young Faculty Award (D18AP00050), ONR (N000141812132) and ARO (W911NF1810300). ...
dblp:conf/icml/Elhamifar19
fatcat:jxm23fztszfiflloepme7w6sva
A NEW MODEL ON AUTOMATIC TEXT SUMMARIZATION FOR TURKISH
2021
Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering
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Also, the effectiveness of widely used features for automatic text summarization in the Turkish language is evaluated using sequential feature selection methods. ...
It becomes challenging and time-consuming for the users to access the information they desire within this increasing amount of data. ...
In this study, two different wrapper methods, including sequential forward selection (SFS) and sequential backward selection (SBS) were used to find the best subset of 8 features explained in the previous ...
doi:10.18038/estubtda.898446
fatcat:fp2gyph7oncb3fjmf5unr57urm
Summary Transfer: Exemplar-based Subset Selection for Video Summarization
[article]
2016
arXiv
pre-print
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We propose a novel subset selection technique that leverages supervision in the form of human-created summaries to perform automatic keyframe-based video summarization. ...
Video summarization has unprecedented importance to help us digest, browse, and search today's ever-growing video collections. ...
Extension for sequential modeling Prior work seqDPP [10] shows that the vanilla DPP is inadequate in capturing the sequential nature of video data. ...
arXiv:1603.03369v3
fatcat:svqls3kgyzfbno5fyfj52bnq2y
Improving Sequential Determinantal Point Processes for Supervised Video Summarization
[article]
2018
arXiv
pre-print
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This paper is in the vein of supervised video summarization using sequential determinantal point process (SeqDPP), which models diversity by a probabilistic distribution. ...
While the ubiquitous video data is a great source for information discovery and extraction, the computational challenges are unparalleled. ...
Disentangling size and content in SeqDPP In this section, we propose a sequential model of generalized DPPs (SeqGDPP) that accepts an arbitrary distribution over the sizes of the subsets whose content ...
arXiv:1807.10957v2
fatcat:hlcslyqpkza43o2gqdyebsjoly
Bayesian network classifiers versus selective -NN classifier
2005
Pattern Recognition
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This subset is established by means of sequential feature selection methods. ...
Experimental results on classifying data of a surface inspection task and data sets from the UCI repository show that Bayesian network classifiers are competitive with selective k-NN classifiers concerning ...
Bouchaffra for the valuable comments on this paper and to Ingo Reindl and Voest Alpine Donawitz Stahl for providing the data for the first experiment. ...
doi:10.1016/j.patcog.2004.05.012
fatcat:6ylgxmcervazld2q2wjl6gun3e
Channel selection methods for the P300 Speller
2014
Journal of Neuroscience Methods
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of channels in use, so it is in the user's interest to use a channel set of modest size. ...
We examine the effect of active channel selection for individuals on speller performance, using generalized standard feature-selection methods, and present a new channel selection method, termed Jumpwise ...
In nearly every case, the subset selected by Max-SNR yields lower accuracy than the standard subset. AUC and Percent Correct scores for the subsets chosen by sequential selection methods. ...
doi:10.1016/j.jneumeth.2014.04.009
pmid:24797224
pmcid:PMC4106671
fatcat:3demnw7fq5hvfbg5sxrwrg4z7i
Practical selection of representative sets of RNA-seq samples using a hierarchical approach
2021
Bioinformatics
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In sequence-based approaches for representative set selection (e.g. a k-mer counting approach that selects a subset based on k-mer similarities between RNA-seq samples), because of the large numbers of ...
We demonstrate that hierarchical representative set selection can achieve summarization quality close to that of direct representative set selection, while largely reducing runtime and memory requirements ...
Cong Ma and Dr. Natalie Sauerwald for useful discussions. Conflict of Interest: C.K. is a co-founder of Ocean Genomics, Inc. ...
doi:10.1093/bioinformatics/btab315
pmid:34252927
fatcat:lpsj3rez2zdllfj25lc7nqgzvm
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