Incremental multi-class semi-supervised clustering regularized by Kalman filtering.

Neural Netw

KU Leuven, ESAT-STADIUS, Kasteelpark Arenberg 10, B-3001 Leuven (Heverlee), Belgium. Electronic address:

Published: November 2015

AI Article Synopsis

  • The paper presents a new on-line semi-supervised learning algorithm based on regularized kernel spectral clustering (KSC) that deals with sequentially arriving data with minimal labeling.
  • It utilizes a few labeled data points as prototypes to enhance label estimation for unlabeled data, making it adaptable for real-time applications.
  • The algorithm shows promising results in experiments, including video segmentation, by effectively tracking moving objects and maintaining smooth cluster evolution over time.

Article Abstract

This paper introduces an on-line semi-supervised learning algorithm formulated as a regularized kernel spectral clustering (KSC) approach. We consider the case where new data arrive sequentially but only a small fraction of it is labeled. The available labeled data act as prototypes and help to improve the performance of the algorithm to estimate the labels of the unlabeled data points. We adopt a recently proposed multi-class semi-supervised KSC based algorithm (MSS-KSC) and make it applicable for on-line data clustering. Given a few user-labeled data points the initial model is learned and then the class membership of the remaining data points in the current and subsequent time instants are estimated and propagated in an on-line fashion. The update of the memberships is carried out mainly using the out-of-sample extension property of the model. Initially the algorithm is tested on computer-generated data sets, then we show that video segmentation can be cast as a semi-supervised learning problem. Furthermore we show how the tracking capabilities of the Kalman filter can be used to provide the labels of objects in motion and thus regularizing the solution obtained by the MSS-KSC algorithm. In the experiments, we demonstrate the performance of the proposed method on synthetic data sets and real-life videos where the clusters evolve in a smooth fashion over time.

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Source
http://dx.doi.org/10.1016/j.neunet.2015.08.001DOI Listing

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