Time Series Classification using the Hidden-Unit Logistic Model
We present a new model for time series classification, called the hidden-unit logistic model, that uses binary stochastic hidden units to model latent structure in the data. The hidden units are connected in a chain structure that models temporal dependencies in the data. Compared to the prior models for time series classification such as the hidden conditional random field, our model can model very complex decision boundaries because the number of latent states grows exponentially with the number of hidden units. We demonstrate the strong performance of our model in experiments on a variety of (computer vision) tasks, including handwritten character recognition, speech recognition, facial expression, and action recognition. We also present a state-of-the-art system for facial action unit detection based on the hidden-unit logistic model.
Code (0)
등록된 구현이 없습니다.
Tasks
Action RecognitionAction Unit DetectionClassificationFacial Action Unit DetectionGeneral Classificationspeech-recognitionSpeech RecognitionTemporal Action LocalizationTime SeriesTime Series AnalysisTime Series ClassificationSimilar Papers 제목 키워드 기반
A regression model with a hidden logistic process for feature extraction from time series
A new approach for feature extraction from time series is proposed in this paper. This approach consists of a specific regression model incorporating a discrete hidden logistic process. The model parameters are estimated…
regressionTime SeriesTime Series AnalysisReconstructing shared dynamics with a deep neural network
Determining hidden shared patterns behind dynamic phenomena can be a game-changer in multiple areas of research. Here we present the principles and show a method to identify hidden shared dynamics from time series by a t…
Time SeriesTime Series AnalysisA Hidden Variables Approach to Multilabel Logistic Regression
Multilabel classification is an important problem in a wide range of domains such as text categorization and music annotation. In this paper, we present a probabilistic model, Multilabel Logistic Regression with Hidden v…
ClassificationGeneral ClassificationregressionText CategorizationBidirectional learning for time-series models with hidden units
Hidden units can play essential roles in modeling time-series having long-term dependency or on-linearity but make it difficult to learn associated parameters. Here we propose a way to learn such a time-series model…
Time SeriesTime Series AnalysisTime series modeling by a regression approach based on a latent process
Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic rep…
global-optimizationregressionTime SeriesTime Series Analysis