A novel sequence representation for unsupervised analysis of human activities

A novel sequence representation for unsupervised analysis of human activities

We present a novel activity representation as bags of event n-grams to extract global structural information of activities using their local event statistics. Exploiting this representation, we present a computational framework for unsupervised activityclass discovery, activity classification and anomalous activity detection. To this end, we model activity-classes as maximally similar activity-...

A computational framework for unsupervised analysis of everyday human activities

Sequence to Sequence Autoencoders for Unsupervised Representation Learning from Audio

This paper describes our contribution to the Acoustic Scene Classification task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2017). We propose a system for this task using a recurrent sequence to sequence autoencoder for unsupervised representation learning from raw audio files. First, we extract mel-spectrograms from the raw audio files. Secon...

a time-series analysis of the demand for life insurance in iran

با توجه به تجزیه و تحلیل داده ها ما دریافتیم که سطح درامد و تعداد نمایندگیها باتقاضای بیمه عمر رابطه مستقیم دارند و نرخ بهره و بار تکفل با تقاضای بیمه عمر رابطه عکس دارند

Unsupervised Analysis of Everyday Human Activities Using Suffix Trees

Traditional approaches for activity modeling assume prior knowledge about the structure of activities, based on which explicitly defined models are learned in a supervised manner. However, such activity structure is generally not completely known a priori. It is therefore imperative to find representations that facilitate learning of this structure with minimal supervision. Recent representatio...