Novelty Detection and Online Learning for Chunk Data Streams

Yi Wang, Yi Ding, Xiangjian He, Xin Fan, Chi Lin, Fengqi Li, Tianzhu Wang, Zhongxuan Luo, Jiebo Luo

Research output: Journal PublicationArticlepeer-review

2 Citations (Scopus)

Abstract

Datastream analysis aims at extracting discriminative information for classification from continuously incoming samples. It is extremely challenging to detect novel data while incrementally updating the model efficiently and stably, especially for high-dimensional and/or large-scale data streams. This paper proposes an efficient framework for novelty detection and incremental learning for unlabeled chunk data streams. First, an accurate factorization-free kernel discriminative analysis (FKDA-X) is put forward through solving a linear system in the kernel space. FKDA-X produces a Reproducing Kernel Hilbert Space (RKHS), in which unlabeled chunk data can be detected and classified by multiple known-classes in a single decision model with a deterministic classification boundary. Moreover, based on FKDA-X, two optimal methods FKDA-CX and FKDA-C are proposed. FKDA-CX uses the micro-cluster centers of original data as the input to achieve excellent performance in novelty detection. FKDA-C and incremental FKDA-C (IFKDA-C) using the class centers of original data as their input have extremely fast speed in online learning. Theoretical analysis and experimental validation on under-sampled and large-scale real-world datasets demonstrate that the proposed algorithms make it possible to learn unlabeled chunk data streams with significantly lower computational costs and comparable accuracies than the state-of-The-Art approaches.

Original languageEnglish
Article number8955936
Pages (from-to)2400-2412
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume43
Issue number7
DOIs
Publication statusPublished - 1 Jul 2021
Externally publishedYes

Keywords

  • Data stream
  • feature selection
  • novelty detection
  • online learning

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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