상세정보
학술지
Partial-Update Dimensionality Reduction for Accumulating Co-Occurrence Events
- 저자
- 나승훈, 이종혁
- 발행일
- 201401
- 출처
- Pattern Recognition Letters, v.36 no.1, pp.62-73
- ISSN
- 0167-8655
- 출판사
- Elsevier
- 협약과제
- 13VS3500, 지식학습 기반의 다국어 확장이 용이한 관광/국제행사 통역률 90%급 자동 통번역 소프트웨어 원천 기술 개발, 김영길
- 초록
- This paper addresses a novel problem when learning similarities. In our problem, an input is given by a long sequence of co-occurrence events among objects, namely a stream of co-occurrence events. Given a stream of co-occurrence events, we learn unknown latent vectors of objects such that their inner product adaptively approximates the target similarities resulting from accumulating co-occurrence events. Toward this end, we propose a new incremental algorithm for dimensionality reduction. The core of our algorithm is its partial updating style where only a small number of latent vectors are modified for each co-occurrence event, while most other latent vectors remain unchanged. Experiment results using both synthetic and real data sets demonstrate that in contrast to some existing methods, the proposed algorithm can stably and gradually learn target similarities among objects without being trapped by the collapsing problem. © 2013 Elsevier B.V. All rights reserved.
- KSP 제안 키워드
- Co-occurrence, Data sets, Experiment results, Inner Product, Partial updating, Real data, dimensionality reduction, incremental algorithm, long sequence, partial update