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Abstract. Time-series classification has attracted increasing interest in recent years, particularly for long time-series as those arising in bioinformatics and financial domain. Many dimensionality reduction algorithms have been proposed to attack the so-called curse of dimensionality problem. However, choosing the number of features is not a trivial task and has not been well considered. In this paper, we propose a novel blind feature extraction algorithm with Haar wavelet transform which can determine the feature dimensionality automatically. The algorithm takes the tradeoff of achieving lower dimensionality and lower sum of squared errors between the features and original time-series. Experimental results performed on several widely used time-series data demonstrate the effectiveness of the proposed algorithm.

The base-free benzoborirene 1,2-BR-1,2-C6H4 (7) and its three-dimensional inorganic analogue 1,2-BR-1,2-C2B10H10 (13) have been successfully synthesized by Cp2ZrBr2 and LiCl elimination, respectively. The Cl analogue of the key intermediate for the formation of benzoborirene 7 has been isolated and structurally characterized, thus suggesting the reaction pathway via benzyne Zr complex formation, B–Br/Cbenzyne–Zr σ-bond metathesis, and a Cp2ZrBr2 elimination/ring-closing process. The rationality of the reaction pathway has been confirmed by DFT calculations. In addition, the title compounds shared the same reactivity pattern (i.e., 1,3-silyl migration) toward MeIiPr (8), thus allowing for the synthetic approach to the first carborane-substituted iminoborane 14.

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