Clustering has been recognized as an important and valuable capability in the data mining field. Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, t...

Clustering has been recognized as an important and valuable capability in the data mining field. Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, t...

Clustering has been recognized as an important and valuable capability in the data mining field. Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, t...

For a bounded linear operator A on a Hilbert space H we study local spectral sets and their relation to the spectrum of the local operator. By the local operator we mean A restricted to a Krylov subspace spanfb; Ab; A 2 b; :::g for a generic b 2 H. Moreover we investigate the relation between (A), the spectrum of A, and the spectrum of the local operator. Also spectral properties of the spectru...

A standard approach to model reduction of large-scale higher-order linear dynamical systems is to rewrite the system as an equivalent first-order system and then employ Krylov-subspace techniques for model reduction of first-order systems. This paper presents some results about the structure of the block-Krylov subspaces induced by the matrices of such equivalent first-order formulations of hig...

We investigate optimal control of linear port-Hamiltonian systems with constraints, in which one aims to perform a state transition minimal energy supply. Decomposing the space into dissipative and non-dissipative (i.e. conservative) subspaces, we show that set reachable states is bounded w.r.t. subspace. prove problem exhibits turnpike property respect subspace, i.e., for varying initial condi...

In this paper we consider adaptive workflow nets, a subclass of nested nets that allows more comfort and expressive power for modelling adaptation and exception handling in workflow nets. We define two important behavioral properties of adaptive workflow nets: soundness and circumspectness. Soundness means that a proper final marking (state) can be reached from any marking which is reachable fr...

This paper presents theory, algorithms, and validation results for system identification of continuous-time statespace models from finite input–output sequences. The algorithms developed are methods of subspace model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input–output model and a stochastic innovations mode...

Learning from demonstration (LfD) is the process of building behavioral models of a task from demonstrations provided by an expert. These models can be used e.g. for system control by generalizing the expert demonstrations to previously unencountered situations. Most LfD methods, however, make strong assumptions about the expert behavior, e.g. they assume the existence of a deterministic optima...

Subspace-based methods for state-space system identiication have lately been suggested as an alternative to more traditional techniques for multivariable system identiication. In this paper a novel instrumental variable (IV) approach to subspace-based system identiication is presented. Consistency and choices of IVs are discussed. One result of the paper is that the so-called PO-MOESP algorithm...