Adaptive clustering uses reinforcement learning to learn the reward values of successive data clusterings. Adaptive clustering applies when external feedback exists for a clustering task. It supports the reuse of clusterings by memorizing what worked well in a previous context. It explores multiple paths in a reinforcement learning environment when the goal is to find better cluster representat...

Clustering ensembles has been recently recognized as an emerging approach to provide more robust solutions to the data clustering problem. Current methods of clustering ensembles typically fall into instance-based, cluster-based, or hybrid approaches; however, most of such methods fail in discriminating among the various clusterings that participate to the ensemble. In this paper, we address th...

A new method of pruning redundant synthesis unit instances in a large-scale synthesis database was proposed based on weighted vector quantization (WVQ). WVQ takes relative importance of each instance into account when clustering the similar instances using vector quantization (VQ) technique. The proposed method was compared with two conventional pruning methods through objective and subjective ...

Part-based representation has been proven to be effective for a variety of visual applications. However, automatic discovery of discriminative parts without object / part-level annotations is challenging. This paper proposes a discriminative mid-level representation paradigm based on the responses of a collection of part detectors, which only requires the imagelevel labels. Towards this goal, w...

Dictionary learning has became an increasingly important task in machine learning, as it is fundamental to the representation problem. A number of emerging techniques specifically include a codebook learning step, in which a critical knowledge abstraction process is carried out. Existing approaches in dictionary (codebook) learning are either generative (unsupervised e.g. k-means) or discrimina...

In the photo retrieval task of ImageCLEF 2008, we examined the influences of image representations and clustering methods for enhancing instance recall when they are used in post-retrieval clustering. Two types of visual concepts and hierarchical and partitioning clustering methods were compared. We used the title fields in the search topics, and either only the title fields or both the title a...

• J. Beringer and E. Hüllermeier. Efficient instance based learning on data streams. Adaptive optimization of the number of clusters in fuzzy clustering. Fuzzy clustering of parallel data streams. Adaptive optimization of the number of clusters in fuzzy clustering.

In high dimensional data, the general performance of traditional clustering algorithms decreases. This is partly because the similarity criterion used by these algorithms becomes inadequate in high dimensional space. Another reason is that some dimensions are likely to be irrelevant or contain noisy data, thus hiding a possible clustering. To overcome these problems, subspace clustering techniq...

This paper presents an investigation into event detection in crowded scenes, where the event of interest co-occurs with other activities and only binary labels at the clip level are available. The proposed approach incorporates a fast feature descriptor from the MPEG domain, and a novel multiple instance learning (MIL) algorithm using sparse approximation and random sensing. MPEG motion vectors...