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Determination of structural characteristics of forests at large scales is an important problem in both scientific studies and development of management practices. Light detection and ranging (LIDAR) waveform data have been demonstrated to be valuable for estimating forest structural parameters even in dense forests, although challenges inherent to the LIDAR acquisition systems must be addressed. A new approach for processing LIDAR waveform data to estimate forest structural parameters is proposed. It was applied to Laser Vegetation Imaging Sensor waveform data acquired over old-growth tropical forest in the La Selva Biological Station, Costa Rica. Linear and nonlinear feature extraction methods were utilized to derive a lower dimensional feature space from high-dimensional LIDAR waveform data. The resulting features were used to estimate mean canopy heights through multiple linear regression analysis. Experimental results obtained by the new approach were statistically comparable to estimates obtained using features extracted via traditional waveform analysis, and the proposed approach successfully discovered another meaningful lower dimensional feature space without manual interpretation.