Papers › Log-time and Log-space Extreme Classification

Log-time and Log-space Extreme Classification

7 Nov 2016arXiv:1611.01964archive 2025-07-28

Kalina Jasinska, Nikos Karampatziakis

We present LTLS, a technique for multiclass and multilabel prediction that can perform training and inference in logarithmic time and space. LTLS embeds large classification problems into simple structured prediction problems and relies on efficient dynamic programming algorithms for inference. We train LTLS with stochastic gradient descent on a number of multiclass and multilabel datasets and show that despite its small memory footprint it is often competitive with existing approaches.

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compute_here_to_sink kjasinska/ltls/utils/graph.py named in the paper unverified MIT (permissive) · 1eb74f74d08a375b · report
count_edge_skips kjasinska/ltls/utils/graph.py named in the paper unverified MIT (permissive) · 11eb2b4c43fdcb99 · report
create_ranking1 kjasinska/ltls/utils/ranking.py named in the paper unverified MIT (permissive) · dfd69c9dcb79e8ba · report
create_ranking2 kjasinska/ltls/utils/ranking.py named in the paper unverified MIT (permissive) · 4013f47fb3f62ce4 · report
gengraph kjasinska/ltls/utils/graph.py named in the paper unverified MIT (permissive) · ececaaa2fec797a3 · report
load_dataset kjasinska/ltls/utils/io.py named in the paper unverified MIT (permissive) · 043d5512de4d7487 · report
merge_rankings kjasinska/ltls/utils/ranking.py named in the paper unverified MIT (permissive) · 6ddbbef5072d7751 · report

Tasks

ClassificationGeneral ClassificationPredictionStructured Prediction

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Methods

Introduced by this paper: LTLS

LTLS

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