Foundations of statistical natural language processing
Foundations of statistical natural language processing
Feature-based classification of time-series data
Information processing and technology
Interval and dynamic time warping-based decision trees
Proceedings of the 2004 ACM symposium on Applied computing
Temporal classification: extending the classification paradigm to multivariate time series
Temporal classification: extending the classification paradigm to multivariate time series
A Multiresolution Symbolic Representation of Time Series
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Smooth minimization of non-smooth functions
Mathematical Programming: Series A and B
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Segmental Hidden Markov Models with Random Effects for Waveform Modeling
The Journal of Machine Learning Research
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Embedding time series data for classification
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
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In this paper, we investigate order-preserving sparse coding for classifying multi-dimensional sequence data. Such a problem is often tackled by first decomposing the input sequence into individual frames and extracting features, then performing sparse coding or other processing for each frame based feature vector independently, and finally aggregating individual responses to classify the input sequence. However, this heuristic approach ignores the underlying temporal order of the input sequence frames, which in turn results in suboptimal discriminative capability. In this work, we introduce a temporal-order-preserving regularizer which aims to preserve the temporal order of the reconstruction coefficients. An efficient Nesterov-type smooth approximation method is developed for optimization of the new regularization criterion, with guaranteed error bounds. Extensive experiments for time series classification on a synthetic dataset, several machine learning benchmarks, and a challenging real-world RGB-D human activity dataset, show that the proposed coding scheme is discriminative and robust, and it outperforms previous art for sequence classification.