The principle of perception redundancy states that by optimally balancing between the information reduction of the input data and sufficient redundancy, classification performance should improve due to smaller search space from the reduced dimensions and noise-invariant from retained redundancy. For dimensionality reduction using global information, principal component analysis (PCA) is a well suited method especially for signal processing task. However, for pattern classification purpose and for image classification in particular, operating on raw input data sometimes limits the benefit of the PCA. Following the expansion-reduction model of data-processing, we propose the use of multiple resolution analysis through continuous wavelet transform (WT) to rearrange input data into different combinations according to wavelet kernel criteria. Quantization further provides intrinsic de-noising result plus sparseness in the transform space which preconditions the orthogonality. PCA is then performed on each level of the data resolution, generating mutually supportive classification discriminants. All together, this multiple resolution principal wavelet component method provides two significant advantages over traditional PCA: i) providing integrated de-noising and redistribution of information content, thereby establishes controlled and mathematically sound downsampling scheme, which alleviates the curse of dimensionality and, at the same time, attenuates noises. ii) Establishing a multiple resolution decision process, whereas each resolution level provides supplemental principal wavelet components, being at least quasi-orthogonal by nature, to support classification with maximum tolerance.