Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relevance of time-frequency features for phonetic and speaker-channel classification
Speech Communication
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Selecting the Bin Size of a Time Histogram
Neural Computation
Computational Statistics & Data Analysis
Information theoretic feature extraction for audio-visual speech recognition
IEEE Transactions on Signal Processing
On estimating mutual information for feature selection
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Static and Dynamic Spectral Features: Their Noise Robustness and Optimal Weights for ASR
IEEE Transactions on Audio, Speech, and Language Processing
Estimation of the information by an adaptive partitioning of the observation space
IEEE Transactions on Information Theory
Computers and Electrical Engineering
Hi-index | 0.10 |
This paper presents a low bias histogram-based estimation of mutual information and its application to feature selection problems. By canceling the first order bias, the estimation avoids the bias accumulation problem that affects classical methods. As a consequence, on a synthetic feature selection problem, only the proposed method results in the exact number of features to be chosen in the Gaussian case when compared to four other approaches. In a speech recognition application, the proposed method and the Sturges method are the only ones that lead to a correct number of selected features in the noise free case. In the reduced data case, only the proposed method points out the optimal number of features to select. Finally, in the noisy case, only the proposed method leads to results of high quality; other methods show severely underestimated numbers of selected features.