BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature\n Selection in Sublinear Memory Article Swipe
Amirali Aghazadeh
,
Vipul Gupta
,
Alex DeWeese
,
O. Ozan Koyluoglu
,
Kannan Ramchandran
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2010.13829
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2010.13829
We consider feature selection for applications in machine learning where the\ndimensionality of the data is so large that it exceeds the working memory of\nthe (local) computing machine. Unfortunately, current large-scale sketching\nalgorithms show poor memory-accuracy trade-off due to the irreversible\ncollision and accumulation of the stochastic gradient noise in the sketched\ndomain. Here, we develop a second-order ultra-high dimensional feature\nselection algorithm, called BEAR, which avoids the extra collisions by storing\nthe second-order gradients in the celebrated Broyden-Fletcher-Goldfarb-Shannon\n(BFGS) algorithm in Count Sketch, a sublinear memory data structure from the\nstreaming literature. Experiments on real-world data sets demonstrate that BEAR\nrequires up to three orders of magnitude less memory space to achieve the same\nclassification accuracy compared to the first-order sketching algorithms.\nTheoretical analysis proves convergence of BEAR with rate O(1/t) in t\niterations of the sketched algorithm. Our algorithm reveals an unexplored\nadvantage of second-order optimization for memory-constrained sketching of\nmodels trained on ultra-high dimensional data sets.\n
Related Topics
Concepts
Sublinear function
Computer science
Algorithm
Curse of dimensionality
Sketch
Feature (linguistics)
Feature selection
Broyden–Fletcher–Goldfarb–Shanno algorithm
Domain (mathematical analysis)
Selection (genetic algorithm)
Rate of convergence
Artificial intelligence
Mathematics
Philosophy
Channel (broadcasting)
Asynchronous communication
Mathematical analysis
Computer network
Linguistics
Metadata
- Type
- preprint
- Landing Page
- http://arxiv.org/abs/2010.13829
- https://arxiv.org/pdf/2010.13829
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4297776600
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4297776600Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2010.13829Digital Object Identifier
- Title
-
BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature\n Selection in Sublinear MemoryWork title
- Type
-
preprintOpenAlex work type
- Publication year
-
2020Year of publication
- Publication date
-
2020-10-26Full publication date if available
- Authors
-
Amirali Aghazadeh, Vipul Gupta, Alex DeWeese, O. Ozan Koyluoglu, Kannan RamchandranList of authors in order
- Landing page
-
https://arxiv.org/abs/2010.13829Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2010.13829Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2010.13829Direct OA link when available
- Concepts
-
Sublinear function, Computer science, Algorithm, Curse of dimensionality, Sketch, Feature (linguistics), Feature selection, Broyden–Fletcher–Goldfarb–Shanno algorithm, Domain (mathematical analysis), Selection (genetic algorithm), Rate of convergence, Artificial intelligence, Mathematics, Philosophy, Channel (broadcasting), Asynchronous communication, Mathematical analysis, Computer network, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.storing\nthe | 66 |
| abstract_inverted_index.t\niterations | 122 |
| abstract_inverted_index.BEAR\nrequires | 92 |
| abstract_inverted_index.Unfortunately, | 27 |
| abstract_inverted_index.the\nstreaming | 83 |
| abstract_inverted_index.memory-accuracy | 33 |
| abstract_inverted_index.sketched\ndomain. | 48 |
| abstract_inverted_index.feature\nselection | 56 |
| abstract_inverted_index.memory-constrained | 136 |
| abstract_inverted_index.the\ndimensionality | 10 |
| abstract_inverted_index.same\nclassification | 105 |
| abstract_inverted_index.sketching\nalgorithms | 30 |
| abstract_inverted_index.unexplored\nadvantage | 131 |
| abstract_inverted_index.irreversible\ncollision | 38 |
| abstract_inverted_index.algorithms.\nTheoretical | 112 |
| abstract_inverted_index.Broyden-Fletcher-Goldfarb-Shannon\n(BFGS) | 72 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile.value | 0.30344572 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |